From 227216723555325d22883f768c2b977c81474d2b Mon Sep 17 00:00:00 2001
From: Ananta Shahane <ananta.shahane@aol.com>
Date: Thu, 18 Jun 2026 14:27:29 +0200
Subject: [PATCH 1/7] Initial commit (MoEH).

---
 iohblade/methods/mcts_ahd/prompts.py |   2 +-
 iohblade/methods/moeh/moeh.py        |  96 ++++++++++++++++++
 iohblade/methods/moeh/population.py  |  24 +++++
 iohblade/methods/moeh/prompts.py     | 145 +++++++++++++++++++++++++++
 iohblade/solution.py                 |  44 +++++---
 tests/unit/test_population.py        |  25 +++++
 tests/unit/test_solution.py          |  59 +++++++++++
 7 files changed, 379 insertions(+), 16 deletions(-)
 create mode 100644 iohblade/methods/moeh/moeh.py
 create mode 100644 iohblade/methods/moeh/population.py
 create mode 100644 iohblade/methods/moeh/prompts.py
 create mode 100644 tests/unit/test_population.py

diff --git a/iohblade/methods/mcts_ahd/prompts.py b/iohblade/methods/mcts_ahd/prompts.py
index 24cc437..3a7b81a 100644
--- a/iohblade/methods/mcts_ahd/prompts.py
+++ b/iohblade/methods/mcts_ahd/prompts.py
@@ -119,7 +119,7 @@ def get_prompt_m1(
 "
             + indiv.code
             + "\n\
-Please create a new algorithm that has a different form but can be a modified version of the provided algorithm. Attempt to introduce more novel mechanisms and new equations or programme segments.\n"
+Please create a new algorithm that is a modified version of the provided algorithm. Attempt to introduce more novel mechanisms and new equations or programme segments.\n"
             "Respond in adherance to following contract:"
         )
         prompt_content += "\n".join([task_prompt, example_prompt, format_prompt])
diff --git a/iohblade/methods/moeh/moeh.py b/iohblade/methods/moeh/moeh.py
new file mode 100644
index 0000000..8198016
--- /dev/null
+++ b/iohblade/methods/moeh/moeh.py
@@ -0,0 +1,96 @@
+from iohblade.llm import LLM
+from iohblade.method import Method
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+
+from iohblade.methods.moeh.prompts import MoEH_Prompts
+from iohblade.methods.moeh.population import Population
+
+class MoEH:
+    def __init__(self,
+                 llm: LLM,
+                 problem: Problem,
+                 iterations: int,
+                 max_sample_nums: int,
+                 population_size: int,
+                 use_e2_operator: bool = True,
+                 use_m1_operator: bool = True,
+                 use_m2_operator: bool = True,
+                 max_workers: int = 2) -> None:
+        """
+            A `Multi-Objective Evolution of Heuristic Using LLLM` implementation for BLADE, as defined in paper: https://arxiv.org/pdf/2409.16867
+
+            ## Params:
+            - `llm: LLM`: Object of one of the final class in `iohblade.llm`.
+            - `problem: Problem`: Object instance of a class that inherits from `iohblade.problem`.
+            - `iterations: int` Max number of iterations for which the algorithm is to be run.
+            - `max_sample_nums: int`: Max number sample that can be generated by the LLM.
+            - `population_size: int`: Population size (λ / µ) for the evolutionary algorithm.
+            - `use_e2_operator: bool(True)`: Use e2 operation as stated in the `moeh.prompts`; multi-individuals -> 1 individual mutation.
+            - `use_m1_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (adds novel methods.)
+            - `use_m2_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (used different approach.)
+            - `max_workers: int(2)`: Declaration of max `loky` workers for `joblib.Parallel`.
+
+            ## Note:
+
+            - The algorithm quits running when either of the conditons are met (||):
+                - `iterations` count of generations are iterated over.
+                - `max_sample_nums` count of samples are generated.
+        """
+        
+        assert max_sample_nums > 1, "Max sample number must be positive integer."
+        assert type(max_sample_nums) == int, "Max sample number must be positive integer."
+        assert population_size > 1, "Population size must be positive integer."
+        assert type(population_size) == int, "Population size must be positive integer."
+        assert iterations > 1, "Iteration count must be positive integer."
+        assert type(iterations) == int, "Iteration count must be positive integer."
+        assert max_workers > 1, "Max Workers must be positive integer."
+        assert type(max_workers) == int, "Max Workers must be positive integer."
+
+        # Accepted parameters.
+        self.llm = llm
+        self.problem = problem
+        self.iterations = iterations
+        self.max_sample_nums = max_sample_nums
+
+        self.max_sample_nums = max_sample_nums
+        self.population_size = population_size
+        self.iterations = iterations
+
+        self.use_e2_operator = use_e2_operator
+        self.use_m1_operator = use_m1_operator
+        self.use_m2_operator = use_m2_operator
+
+        config = self.problem.get_config()
+        self.minimisation = config['minimisation']
+
+
+        # Runtime parameters:
+        self._current_iteration = 0
+        self.population = Population(population_size)
+
+    def initialise(self):
+        """
+            Initialises the evolutionary algorithm.
+
+            # Params:
+                - `None`
+
+            # Returns:
+                - Inline function, doesn't return anything, updates `self.population`.
+        """
+        prompt = MoEH_Prompts.get_prompt_i1(self.problem.task_prompt, 
+                                            self.problem.example_prompt, 
+                                            self.problem.format_prompt)
+        
+        #TODO: Parallel this.....
+        while (len(self.population)  < self.population_size) and self.max_sample_nums > 0:
+            individual = self.llm.sample_solution([prompt])
+            individual = self.problem(individual)
+            self.population.append(individual)
+            self.max_sample_nums -= 1
+        
+    
+
+
+
diff --git a/iohblade/methods/moeh/population.py b/iohblade/methods/moeh/population.py
new file mode 100644
index 0000000..a01749f
--- /dev/null
+++ b/iohblade/methods/moeh/population.py
@@ -0,0 +1,24 @@
+from iohblade.solution import Solution
+
+class Population:
+    def __init__(self, size) -> None:
+        self.size = size
+        self._population: list[Solution] = []
+
+    def append(self, element: Solution):
+        if element.fitness_is_valid():
+            self._population.append(element)
+    
+
+    def __len__(self):
+        return len(self._population)
+    
+    def __getitem__(self, key):
+        return self._population[key]
+
+    def __setitem__(self, key, value):
+        self._population[key] = value
+
+    @property 
+    def population(self):
+        return self._population
diff --git a/iohblade/methods/moeh/prompts.py b/iohblade/methods/moeh/prompts.py
new file mode 100644
index 0000000..9308f87
--- /dev/null
+++ b/iohblade/methods/moeh/prompts.py
@@ -0,0 +1,145 @@
+from iohblade.solution import Solution
+
+
+class MoEH_Prompts:
+    """
+    An extension of `iohblade.Problem` instanced that adds necessary prompt mutation in algorithm including
+    `{i1, e1, e2, m1, m2}`.
+    """
+
+    @classmethod
+    def get_desctiption_prompt(cls, task_prompt: str, solution: Solution) -> str:
+        prompt_content = (
+            task_prompt
+            + "\n"
+            + "Following is the a Code implementing a heuristic algorithm with function name "
+            + solution.name
+            + " to solve the above mentioned problem.\n"
+        )
+        prompt_content += "\n\nCode:\n" + solution.code
+        prompt_content += "\n\nNow you should describe the Design Idea of the algorithm using less than 5 sentences.\n"
+        prompt_content += "Hint: You should highlight every meaningful designs in the provided code and describe their ideas. You can analyse the code to see which variables are given higher values and which variables are given lower values, the choice of parameters or the total structure of the code."
+        return prompt_content
+
+    @classmethod
+    def get_prompt_refine(cls, task_prompt, solution: Solution):
+        prompt_content = (
+            task_prompt
+            + "\n"
+            + "Following is the Design Idea of a heuristic algorithm for the problem and the code with function name '"
+            + solution.name
+            + "' for implementing the heuristic algorithm.\n"
+        )
+        prompt_content += "\nDesign Idea:\n" + solution.description
+        prompt_content += "\n\nCode:\n" + solution.code
+        prompt_content += "\n\nThe content of the Design Idea idea cannot fully represent what the algorithm has done informative. So, now you should re-describe the algorithm using less than 3 sentences.\n"
+        prompt_content += "Hint: You should reference the given Design Idea and highlight the most critical design ideas of the code. You can analyse the code to describe which variables are given higher priorities and which variables are given lower priorities, the parameters and the structure of the code."
+        return prompt_content
+
+    @classmethod
+    def get_prompt_i1(cls, task_prompt, example_prompt, format_prompt):
+        return "\n".join([task_prompt, example_prompt, format_prompt])
+
+    @classmethod
+    def get_prompt_e1(
+        cls, task_prompt, example_prompt, format_prompt, indivs: list[Solution]
+    ):
+        prompt_indiv = (
+            "\n".join(
+                list(
+                    map(
+                        lambda ind: f"```python {ind.code}```\nObjective Value={ind.fitness}\n",
+                        indivs,
+                    )
+                )
+            )
+            + "\n"
+        )
+
+        prompt_content = (
+            "\n"
+            "I have "
+            + str(len(indivs))
+            + " existing algorithms with their codes as follows: \n\n"
+            + prompt_indiv
+            + "Please create a new algorithm that has a totally different form from the given algorithms. Try generating codes with different structures, flows or algorithms. The new algorithm should have a relatively low objective value, will adhering to following contract. \n"
+            + task_prompt
+            + example_prompt
+            + format_prompt
+        )
+
+        return prompt_content
+
+    @classmethod
+    def get_prompt_e2(
+        cls, task_prompt, example_prompt, format_prompt, indivs: list[Solution]
+    ):
+        prompt_indiv = "\n".join(
+            list(
+                map(
+                    lambda item: f"""
+No. {item[0] + 1} with description: {item[1].description}
+and code
+```python
+{item[1].code}
+```
+Has objective Value {item[1].fitness}.
+""",
+                    enumerate(indivs),
+                )
+            )
+        )
+
+        prompt_content = (
+            "\n"
+            "I have "
+            + str(len(indivs))
+            + " existing algorithms with their codes and objective values as follows: \n\n"
+            + prompt_indiv
+            + f"Please create a new algorithm that has a similar form to the No.{len(indivs)} algorithm and is inspired by the No.{1} algorithm. The new algorithm should have a objective value lower than both algorithms.\n"
+            f"Firstly, list the common ideas in the No.{1} algorithm that may give good performances. Secondly, based on the common idea, describe the design idea based on the No.{len(indivs)} algorithm and main steps of your algorithm in one sentence. \
+The description must be inside a brace. Thirdly, reply with a response adhering to following contract. Make sure only the final code is in code block. \
+'"
+            + task_prompt
+            + example_prompt
+            + format_prompt
+        )
+        return prompt_content
+
+    @classmethod
+    def get_prompt_m1(
+        cls, task_prompt, example_prompt, format_prompt, indiv: Solution
+    ):
+        prompt_content = (
+            "I have one algorithm with its code as follows. \n\n\
+Algorithm's description: "
+            + indiv.description
+            + "\n\
+Code:\n\
+"
+            + indiv.code
+            + "\n\
+Please create a new algorithm that is a modified version of the provided algorithm. Attempt to introduce more novel mechanisms and new equations or programme segments.\n"
+            "Respond in adherance to following contract:"
+        )
+        prompt_content += "\n".join([task_prompt, example_prompt, format_prompt])
+        return prompt_content
+
+    @classmethod
+    def get_prompt_m2(
+        cls, task_prompt, example_prompt, format_prompt, indiv: Solution
+    ):
+        prompt_content = (
+            "I have one algorithm with its code as follows. \n\n\
+Algorithm's description: "
+            + indiv.description
+            + "\n\
+Code:\n\
+"
+            + indiv.code
+            + "\n\
+Please identify the main algorithm parameters and help me in creating a new algorithm that has different parameter settings to equations compared to the provided algorithm. \n"
+            "Respond in adherance to following contract:"
+        )
+        prompt_content += "\n".join([task_prompt, example_prompt, format_prompt])
+        return prompt_content
diff --git a/iohblade/solution.py b/iohblade/solution.py
index dfaf42a..e5fb2bc 100644
--- a/iohblade/solution.py
+++ b/iohblade/solution.py
@@ -66,6 +66,19 @@ def __setstate__(self, state):
         if self.configspace == "":
             self.configspace = None
 
+    def fitness_is_valid(self) -> bool:
+        """
+            Checks validity of fitness.
+        """
+        if isinstance(self.fitness, Fitness):
+            for value in self.fitness.to_vector():
+                if np.isnan(value) or np.isinf(value):
+                    return False
+        else:
+            if np.isnan(self.fitness) or np.isinf(self.fitness):
+                return False
+        return True
+
     def set_operator(self, operator):
         """
         Sets the operator name that generated this individual.
@@ -101,7 +114,7 @@ def set_scores(self, fitness, feedback="", error=""):
         return self
 
     def set_scores(
-        self, fitness: float, feedback="", error: Optional[Exception] = None
+        self, fitness: float| Fitness, feedback="", error: Optional[Exception] = None
     ):
         """
             Set the score of current instance of individual.
@@ -120,20 +133,21 @@ def set_scores(
 
             error_type = type(error).__name__
             error_msg = str(error)
-            self.error = repr(error)
-
-            tb = traceback.extract_tb(error.__traceback__)[-1]
-
-            if tb.filename in ("<string>", self.name):
-                code_lines = self.code.splitlines()
-                line_no = tb.lineno
-
-                if 1 <= line_no <= len(code_lines):
-                    code_line = code_lines[line_no - 1]
-                    self.error = (
-                        f"{error_type}: {error_msg}.\n"
-                        f"On line {line_no}: {code_line}.\n"
-                    )
+            try:
+                tb = traceback.extract_tb(error.__traceback__)[-1]
+
+                if tb.filename in ("<string>", self.name):
+                    code_lines = self.code.splitlines()
+                    line_no = tb.lineno
+
+                    if 1 <= line_no <= len(code_lines):
+                        code_line = code_lines[line_no - 1]
+                        self.error = (
+                            f"{error_type}: {error_msg}.\n"
+                            f"On line {line_no}: {code_line}.\n"
+                        )
+            except:
+                self.error = repr(error)
 
         return self
 
diff --git a/tests/unit/test_population.py b/tests/unit/test_population.py
new file mode 100644
index 0000000..c6449bd
--- /dev/null
+++ b/tests/unit/test_population.py
@@ -0,0 +1,25 @@
+from iohblade.solution import Solution
+from iohblade.methods.moeh.population import Population
+
+def test_population_accepts_valid_solution():
+    s = Solution()
+
+    s.set_scores(
+        0.72,
+        "Scored 0.72, best known solution is 0.912."
+    )
+
+    p = Population(10)
+    p.append(s)
+
+    assert len(p) == 1
+    
+
+def test_population_rejects_invalid_solution():
+    s = Solution()
+
+    p = Population(10)
+
+    p.append(s)
+
+    assert len(p) == 0
\ No newline at end of file
diff --git a/tests/unit/test_solution.py b/tests/unit/test_solution.py
index f7abdb7..0eca08b 100644
--- a/tests/unit/test_solution.py
+++ b/tests/unit/test_solution.py
@@ -150,3 +150,62 @@ def test_solution_pickle_roundtrip_multiobjective():
     assert s2.fitness["f2"] == pytest.approx(-1.5)
     # Pareto comparison must work without TypeError
     assert not (s2.fitness < s2.fitness)  # equal → not strictly dominated
+
+
+def test_empty_solution_fitness_validity():
+    s = Solution()
+
+    assert not s.fitness_is_valid(), "Empty solution should not have valid solution."
+
+def test_scalar_solution_fitness_validity():
+    s = Solution()
+    s.set_scores(
+        0.5, "Scored 0.5, best known score is 0.991"
+    )
+
+    assert s.fitness_is_valid(), "Scalar solution has valid fitness, not invalid."
+
+    s.set_scores(
+        float('inf'),
+        'Import failure nonepy is not a library.',
+        Exception('Cannot find nonepy.')
+    )
+
+    assert not s.fitness_is_valid(), "±inf is invalid solution."
+    
+    s.set_scores(
+        float('-inf'),
+        'Import failure nonepy is not a library.',
+        Exception('Cannot find nonepy.')
+    )
+
+    assert not s.fitness_is_valid(), "±inf is invalid solution."
+
+def test_fitness_type_invalidity():
+    s = Solution()
+    s.set_scores(
+        Fitness({
+            'distance': 788,
+            'fuel': float('inf')
+        }),
+        "Got Distance = 788 km, and Fuel calculation failed."
+    )
+    assert not s.fitness_is_valid(), 'Fitness type with invalid values, expects invalidity.'
+
+    s.set_scores(
+        Fitness({
+            'distance': 788,
+            'fuel': 917
+        }),
+        "Got Distance = 788 km, and Fuel = 917 mL."
+    )
+    assert s.fitness_is_valid(), 'Fitness type with valid values, expects validity.'
+    
+    s.set_scores(
+        Fitness({
+            'distance': 788,
+            'fuel': float('nan')
+        }),
+        "Got Distance = 788 km, and Fuel capture failed."
+    )
+    assert not s.fitness_is_valid(), 'Fitness type with invalid values, expects invalidity.'

From b4b47a7d7f50b50f18290d0f043a42b870a0acfe Mon Sep 17 00:00:00 2001
From: Ananta Shahane <ananta.shahane@aol.com>
Date: Wed, 24 Jun 2026 12:19:14 +0200
Subject: [PATCH 2/7] Implementation completed, deep testing.

---
 iohblade/fitness.py                 |  18 +-
 iohblade/methods/moeh/moeh.py       | 107 +++-
 iohblade/methods/moeh/population.py |  76 ++-
 iohblade/methods/moeh/prompts.py    |  40 +-
 iohblade/solution.py                |  18 +-
 pyproject.toml                      |   3 +
 tests/unit/test_population.py       |  39 +-
 uv.lock                             | 800 +++++++++++++++++-----------
 8 files changed, 711 insertions(+), 390 deletions(-)

diff --git a/iohblade/fitness.py b/iohblade/fitness.py
index 1dd763f..69958e6 100644
--- a/iohblade/fitness.py
+++ b/iohblade/fitness.py
@@ -45,32 +45,32 @@ def _dominates(self, other: "Fitness") -> tuple[bool, bool]:
         strictly_better = any(self[k] < other[k] for k in self.keys())
         return better_or_equal, strictly_better
 
-    def __eq__(self, other: object) -> bool:
+    def __eq__(self, other) -> bool:
         if not isinstance(other, Fitness):
             return False
         return self._fitness == other._fitness
 
-    def __lt__(self, other: "Fitness") -> bool:
+    def __lt__(self, other) -> bool:
         if not isinstance(other, Fitness):
-            return NotImplemented
+            return False
         be, sb = self._dominates(other)
         return be and sb
 
-    def __gt__(self, other: "Fitness") -> bool:
+    def __gt__(self, other) -> bool:
         if not isinstance(other, Fitness):
-            return NotImplemented
+            return False
         be, sb = other._dominates(self)
         return be and sb
 
-    def __le__(self, other: "Fitness") -> bool:
+    def __le__(self, other) -> bool:
         if not isinstance(other, Fitness):
-            return NotImplemented
+            return False
         be, sb = self._dominates(other)
         return be or sb
 
-    def __ge__(self, other: "Fitness") -> bool:
+    def __ge__(self, other) -> bool:
         if not isinstance(other, Fitness):
-            return NotImplemented
+            return False
         be, sb = other._dominates(self)
         return be or sb
 
diff --git a/iohblade/methods/moeh/moeh.py b/iohblade/methods/moeh/moeh.py
index 8198016..b1d7c29 100644
--- a/iohblade/methods/moeh/moeh.py
+++ b/iohblade/methods/moeh/moeh.py
@@ -1,3 +1,7 @@
+import random
+import numpy as np
+from enum import Enum
+
 from iohblade.llm import LLM
 from iohblade.method import Method
 from iohblade.problem import Problem
@@ -6,6 +10,12 @@
 from iohblade.methods.moeh.prompts import MoEH_Prompts
 from iohblade.methods.moeh.population import Population
 
+class MutationType(Enum):
+    E2 = 'e2'
+    M1 = 'm1'
+    M2 = 'm2'
+
+
 class MoEH:
     def __init__(self,
                  llm: LLM,
@@ -57,17 +67,24 @@ def __init__(self,
         self.population_size = population_size
         self.iterations = iterations
 
-        self.use_e2_operator = use_e2_operator
-        self.use_m1_operator = use_m1_operator
-        self.use_m2_operator = use_m2_operator
-
         config = self.problem.get_config()
         self.minimisation = config['minimisation']
 
 
         # Runtime parameters:
         self._current_iteration = 0
-        self.population = Population(population_size)
+        self.population = Population(population_size, self.minimisation)
+
+        # Mutation Types
+        self.allowed_mutation_types: list[MutationType] = []
+        if use_e2_operator:
+            self.allowed_mutation_types.append(MutationType.E2)
+        if use_m1_operator:
+            self.allowed_mutation_types.append(MutationType.M1)
+        if use_m2_operator:
+            self.allowed_mutation_types.append(MutationType.M2)
+
+        assert len(self.allowed_mutation_types) >= 1, "Atleast one mutation type is expected, use_x_operator all set false in invalid state."
 
     def initialise(self):
         """
@@ -86,11 +103,83 @@ def initialise(self):
         #TODO: Parallel this.....
         while (len(self.population)  < self.population_size) and self.max_sample_nums > 0:
             individual = self.llm.sample_solution([prompt])
-            individual = self.problem(individual)
+            individual = self.evaluate(individual)
             self.population.append(individual)
             self.max_sample_nums -= 1
+    
+    def evaluate(self, individual: Solution):
+        return self.problem(individual)
+    
+    def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
+        """
+            Queries and returns a new LLM written solution using one of the MutationTypes.
         
+            ## Parameters:
+            - `type: MutationType`: Expects an enum typed `MutationType` to decide which prompt to use for an evolution.
+            - `pop: list[Solution]`: Expects an array of solution of size 1 for `M1` and `M2` types, and of arbitrary size [1, n] for `E2`.
+
+            ## Expectations:
+            - For each `MutationType` following size of population is expected:
+                - `M1`: 1
+                - `M2`: 1
+                - `E2`: [1, n]
+
+            ## Returns:
+            - `Solution`: an evolved solution generated by LLM using given pop and `MutationType`.
+        """
+        match(type):
+            case MutationType.M1:
+                prompt = MoEH_Prompts.get_prompt_m1(
+                    self.problem.task_prompt, 
+                    self.problem.example_prompt, 
+                    self.problem.format_prompt, 
+                    pop[0])
+                solution = self.llm.sample_solution([prompt], parent_ids=[pop[0]])
+                return solution
+            case MutationType.M2:
+                prompt = MoEH_Prompts.get_prompt_m2(self.problem.task_prompt, 
+                    self.problem.example_prompt, 
+                    self.problem.format_prompt, 
+                    pop[0])
+                solution = self.llm.sample_solution([prompt], parent_ids=[pop[0]])
+                return solution
+
+            case MutationType.E2:
+                prompt = MoEH_Prompts.get_prompt_e2(self.problem.task_prompt, 
+                    self.problem.example_prompt, 
+                    self.problem.format_prompt, 
+                    pop)
+                solution = self.llm.sample_solution([prompt], parent_ids=[pop[i] for i in range(len(pop))])
+                return solution
+
+    def evolve_solution(self, population_index: int) -> Solution:
+        mutation_type = random.choice(self.allowed_mutation_types)
+        pop = self.population.population
+        match(mutation_type):
+            case MutationType.E2:
+                solution = self.query_individual(mutation_type, pop)
+            case _:
+                solution = self.query_individual(mutation_type, [pop[population_index]])
+        solution = self.evaluate(solution)
+        self.max_sample_nums -= 1
+        return solution
     
-
-
-
+    def run(self) -> Solution:
+        generation = 0
+        new_population : list[Solution] = []
+
+        self.initialise()
+        while((self.iterations < generation) and (self.max_sample_nums > 0)):
+            new_population = []
+            self.population.parent_selection(self.population_size)
+            for index in range(len(self.population.population)):
+                new_individual = self.evolve_solution(index)
+                new_population.append(new_individual)
+            
+            for individual in new_population:
+                self.population.append(individual)
+            
+            _ = self.population.population_management()
+        
+        return self.population.get_best()
+            
diff --git a/iohblade/methods/moeh/population.py b/iohblade/methods/moeh/population.py
index a01749f..3af22e6 100644
--- a/iohblade/methods/moeh/population.py
+++ b/iohblade/methods/moeh/population.py
@@ -1,9 +1,12 @@
+from codebleu.syntax_match import calc_syntax_match
+import numpy as np
 from iohblade.solution import Solution
 
 class Population:
-    def __init__(self, size) -> None:
+    def __init__(self, size, minimisation: bool) -> None:
         self.size = size
         self._population: list[Solution] = []
+        self.minimisation = minimisation
 
     def append(self, element: Solution):
         if element.fitness_is_valid():
@@ -22,3 +25,74 @@ def __setitem__(self, key, value):
     @property 
     def population(self):
         return self._population
+
+    def parent_selection(self, d: int, test: bool=False) -> list[Solution]:
+        N = self.size
+        S = np.zeros((N, N), dtype=float)
+
+        for i in range(N):
+            for j in range(N):
+                if i != j:
+                    S[i, j] = -calc_syntax_match(
+                        self._population[i].code,
+                        self._population[j].code,
+                        "python"
+                    )
+
+                    if self._population[i].fitness < self._population[j].fitness and not self.minimisation:
+                        S[i, j] = 0
+                    elif self._population[i].fitness > self._population[j].fitness and self.minimisation:
+                        S[i, j] = 0
+
+        v = np.sum(S, axis=0)
+        if test:
+            print(f"S: {S}")
+        v = v - np.max(v)
+        if test:
+            print(f'v: {v}')
+        pdf = np.exp(v)
+        pdf = pdf / pdf.sum()
+        if test:
+            print(f'Softmax: {pdf}')
+        idx = np.random.choice(N, size=d, replace=False, p=pdf)
+        return [self._population[i] for i in idx]
+    
+    def population_management(self, test: bool = False) -> list[Solution]:
+        S = len(self._population)
+        M = np.zeros((S, S), dtype=float)
+        for i in range(S):
+            for j in range(S):
+                if i != j:
+                    M[i][j] = -calc_syntax_match(
+                        self._population[i].code,
+                        self._population[j].code,
+                        'python'
+                    )
+                if self._population[i].fitness < self._population[j].fitness and not self.minimisation:
+                    M[i][j] = 0
+                elif self._population[i].fitness > self._population[j].fitness and self.minimisation:
+                    M[i][j] = 0
+        if test:
+            print('Selection Matrix:')
+            print(M)
+        v = np.sum(M, axis=0)
+        if test:
+            print('Column wise sum:')
+            print(v)
+        k = list(map
+                    (lambda y: y[0], 
+                        sorted(enumerate(v), key=lambda x: x[1], reverse=True)
+                    )
+                )
+        if test:
+            print('Sorted Index:')
+            print(k)
+        new_population : list[Solution] = [self._population[i] for i in k]
+        self._population = new_population
+        return self._population
+    
+    def get_best(self) -> Solution:
+        population = list(sorted(self._population, key=lambda x: x.fitness))
+        if self.minimisation:
+            return population[0]
+        return population[-1]
\ No newline at end of file
diff --git a/iohblade/methods/moeh/prompts.py b/iohblade/methods/moeh/prompts.py
index 9308f87..05a8848 100644
--- a/iohblade/methods/moeh/prompts.py
+++ b/iohblade/methods/moeh/prompts.py
@@ -37,43 +37,13 @@ def get_prompt_refine(cls, task_prompt, solution: Solution):
         return prompt_content
 
     @classmethod
-    def get_prompt_i1(cls, task_prompt, example_prompt, format_prompt):
+    def get_prompt_i1(cls, task_prompt, example_prompt, format_prompt) -> str:
         return "\n".join([task_prompt, example_prompt, format_prompt])
 
-    @classmethod
-    def get_prompt_e1(
-        cls, task_prompt, example_prompt, format_prompt, indivs: list[Solution]
-    ):
-        prompt_indiv = (
-            "\n".join(
-                list(
-                    map(
-                        lambda ind: f"```python {ind.code}```\nObjective Value={ind.fitness}\n",
-                        indivs,
-                    )
-                )
-            )
-            + "\n"
-        )
-
-        prompt_content = (
-            "\n"
-            "I have "
-            + str(len(indivs))
-            + " existing algorithms with their codes as follows: \n\n"
-            + prompt_indiv
-            + "Please create a new algorithm that has a totally different form from the given algorithms. Try generating codes with different structures, flows or algorithms. The new algorithm should have a relatively low objective value, will adhering to following contract. \n"
-            + task_prompt
-            + example_prompt
-            + format_prompt
-        )
-
-        return prompt_content
-
     @classmethod
     def get_prompt_e2(
         cls, task_prompt, example_prompt, format_prompt, indivs: list[Solution]
-    ):
+    ) -> str:
         prompt_indiv = "\n".join(
             list(
                 map(
@@ -109,7 +79,7 @@ def get_prompt_e2(
     @classmethod
     def get_prompt_m1(
         cls, task_prompt, example_prompt, format_prompt, indiv: Solution
-    ):
+    ) -> str:
         prompt_content = (
             "I have one algorithm with its code as follows. \n\n\
 Algorithm's description: "
@@ -127,8 +97,8 @@ def get_prompt_m1(
 
     @classmethod
     def get_prompt_m2(
-        cls, task_prompt, example_prompt, format_prompt, indiv: Solution
-    ):
+        cls, task_prompt: str, example_prompt: str, format_prompt: str, indiv: Solution
+    ) -> str:
         prompt_content = (
             "I have one algorithm with its code as follows. \n\n\
 Algorithm's description: "
diff --git a/iohblade/solution.py b/iohblade/solution.py
index e5fb2bc..efff49d 100644
--- a/iohblade/solution.py
+++ b/iohblade/solution.py
@@ -43,7 +43,7 @@ def __init__(
         self.description = description
         self.configspace = configspace
         self.generation = generation
-        self.fitness = float("nan")
+        self.fitness: float | Fitness = float("nan")
         self.feedback = ""
         self.error = ""
         self.parent_ids = parent_ids
@@ -70,14 +70,12 @@ def fitness_is_valid(self) -> bool:
         """
             Checks validity of fitness.
         """
-        if isinstance(self.fitness, Fitness):
-            for value in self.fitness.to_vector():
-                if np.isnan(value) or np.isinf(value):
-                    return False
-        else:
-            if np.isnan(self.fitness) or np.isinf(self.fitness):
-                return False
-        return True
+        fitness_values = (
+            self.fitness.to_vector()
+            if isinstance(self.fitness, Fitness)
+            else [self.fitness]
+        )
+        return all(np.isfinite(v) for v in fitness_values)
 
     def set_operator(self, operator):
         """
@@ -107,7 +105,7 @@ def get_metadata(self, key):
         """
         return self.metadata[key] if key in self.metadata.keys() else None
 
-    def set_scores(self, fitness, feedback="", error=""):
+    def set_scores(self, fitness: float | Fitness, feedback="", error=""):
         self.fitness = fitness
         self.feedback = feedback
         self.error = error
diff --git a/pyproject.toml b/pyproject.toml
index 5f79fa6..fdbee67 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -65,6 +65,8 @@ docs = [
 methods = [
     "eoh",
     "reevo",
+    "codebleu",
+    "tree-sitter-python"
 ]
 extraproblems = [
     "pymoosh==3.2",
@@ -93,6 +95,7 @@ eoh = { git = "https://github.com/nikivanstein/EoH", subdirectory = "eoh" , rev
 reevo = { git = "https://github.com/nikivanstein/reevo.git", rev = "main" }
 kernel-tuner = { git = "https://github.com/KernelTuner/kernel_tuner.git", rev = "ad41cfe99aec44c99ae79b4ececcc7227eb20db2" }
 autotuning_methodology = { git = "https://github.com/AutoTuningAssociation/autotuning_methodology.git", rev = "5536c5a4a82fa6f3f14d36e38b8e32baf00b6347" }
+codebleu = { git = "https://github.com/anantashahane/codebleu", rev="main"}
 
 [build-system]
 requires = ["hatchling"]
diff --git a/tests/unit/test_population.py b/tests/unit/test_population.py
index c6449bd..6914d9c 100644
--- a/tests/unit/test_population.py
+++ b/tests/unit/test_population.py
@@ -9,7 +9,7 @@ def test_population_accepts_valid_solution():
         "Scored 0.72, best known solution is 0.912."
     )
 
-    p = Population(10)
+    p = Population(10, True)
     p.append(s)
 
     assert len(p) == 1
@@ -18,8 +18,41 @@ def test_population_accepts_valid_solution():
 def test_population_rejects_invalid_solution():
     s = Solution()
 
-    p = Population(10)
+    p = Population(10, True)
 
     p.append(s)
 
-    assert len(p) == 0
\ No newline at end of file
+    assert len(p) == 0
+
+def test_population_selection_matrix():
+    p = Population(3, True)
+    solutions = [Solution("ABC"), Solution("ABD"), Solution("BATMAN")]
+    solutions[0].set_scores(1, "Score is 1")
+    solutions[1].set_scores(2, "Score is 2")
+    solutions[2].set_scores(3, "Score is 3")
+
+    for solution in solutions:
+        p.append(solution)
+    
+    next_gen = p.parent_selection(2, True)
+    for individual in next_gen:
+        assert individual in p
+
+def test_population_management():
+    p = Population(3, True)
+    solutions = [Solution("ABC"), Solution("ABD"), Solution("BATMAN")]
+    solutions[0].set_scores(1, "Score is 1")
+    solutions[1].set_scores(2, "Score is 2")
+    solutions[2].set_scores(3, "Score is 3")
+
+    for solution in solutions:
+        p.append(solution)
+    
+    sorted_population = p.population_management(True)
+    solutions = sorted(solutions, key=lambda x: x.fitness)
+    for i in range(len(solutions)):
+        ref_dict = solutions[i].__dict__
+        source_dict = sorted_population[i].__dict__
+        for key in source_dict:
+            if key != 'id':
+                assert source_dict[key] == ref_dict[key]
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index dc5d13f..ceba125 100644
--- a/uv.lock
+++ b/uv.lock
@@ -181,7 +181,8 @@ dependencies = [
     { name = "networkx" },
     { name = "numpy" },
     { name = "rtree" },
-    { name = "scipy" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "shapely" },
     { name = "trimesh" },
 ]
@@ -226,7 +227,7 @@ wheels = [
 
 [[package]]
 name = "anthropic"
-version = "0.109.2"
+version = "0.111.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -238,9 +239,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1b/b7/9a8e2f79011e89dd6eeb599c27332aed765dac9d6fbee3a55e68e4e3ec25/anthropic-0.109.2.tar.gz", hash = "sha256:d37db299597c7bc124b49b767ff135f1e6456b64af2b2fad4b63b2a1df333cf0", size = 927559, upload-time = "2026-06-15T17:30:25.024Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/8a/9afc7305a2ce4b52b30e137f83cd2a6a90b918b3997073db11bb5a1de55a/anthropic-0.111.0.tar.gz", hash = "sha256:39cbda0ac17a6d423e5bf609811bd69b26eddf6299d7a468126e05bc711ce826", size = 934001, upload-time = "2026-06-18T17:31:44.733Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d1/f2/bee5de8a2699fc8a3cce34d61c7a2626a2c310ddde7ea5611327eb0ddbe9/anthropic-0.109.2-py3-none-any.whl", hash = "sha256:e0fb4ca5df0ed983248c9c6c3242adc81d9cfddb8725902da53698554117abac", size = 923800, upload-time = "2026-06-15T17:30:23.124Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/bb/09e82a81885d787f350fb55ca9df865b63140dd28b3b5b3104c4ae261657/anthropic-0.111.0-py3-none-any.whl", hash = "sha256:c14edb36ed80da9099acbd26b5cec810d76606c31f32a0d56a4cf9d4fa9e25ae", size = 929774, upload-time = "2026-06-18T17:31:43.116Z" },
 ]
 
 [[package]]
@@ -366,7 +367,8 @@ dependencies = [
     { name = "numpy" },
     { name = "progressbar2" },
     { name = "scikit-learn" },
-    { name = "scipy" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "yappi" },
 ]
 
@@ -457,7 +459,7 @@ wheels = [
 
 [[package]]
 name = "blosc2"
-version = "4.5.0"
+version = "4.5.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "msgpack" },
@@ -471,35 +473,35 @@ dependencies = [
     { name = "textual-plotext" },
     { name = "threadpoolctl", marker = "platform_machine != 'wasm32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9c/85/74e9393c66450bb6c7a5d79c55e6035b8e70e74759d196bc01e4e4280057/blosc2-4.5.0.tar.gz", hash = "sha256:570fee6a488d319953947f713c42a0c88d5512476261589fb30610c94e64087a", size = 5529001, upload-time = "2026-06-15T12:03:09.64Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/56/78/affd122d0fdc17969133bd62821a308b6fc95ba57bb16df5f082806e8d04/blosc2-4.5.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8165f741ce8cc0706d60795efe965c682374858236498542aaeb8a98fe8139f0", size = 5948599, upload-time = "2026-06-15T12:02:19.12Z" },
-    { url = "https://files.pythonhosted.org/packages/29/02/ad7e712060c413a91e0e77a24d0cc191db52b64a5dc6230d5ea5698779bd/blosc2-4.5.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ce8a6e0b0be01ab8445c6b8dc931c4df4f91032da0b1bc82104b1f50152c92e6", size = 5152278, upload-time = "2026-06-15T12:02:21.866Z" },
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     { name = "nvidia-nccl-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" },
-    { name = "scipy" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e2/5e/860a1ef13ce38db8c257c83e138be64bcffde8f401e84bf1e2e91838afa3/xgboost-2.1.4.tar.gz", hash = "sha256:ab84c4bbedd7fae1a26f61e9dd7897421d5b08454b51c6eb072abc1d346d08d7", size = 1091127, upload-time = "2025-02-06T18:18:20.192Z" }
 wheels = [

From d8320b9c3674ad6aed5b0c4f6accc7ce6e978867 Mon Sep 17 00:00:00 2001
From: Ananta Shahane <ananta.shahane@aol.com>
Date: Thu, 25 Jun 2026 14:28:05 +0200
Subject: [PATCH 3/7] Testing...

---
 iohblade/methods/__init__.py        |   3 +
 iohblade/methods/moeh/__init__.py   |   3 +
 iohblade/methods/moeh/moeh.py       | 149 +++++++++++++++++++++++++---
 iohblade/methods/moeh/population.py |  12 ++-
 iohblade/problem.py                 |   2 +
 pyproject.toml                      |   2 +-
 tests/unit/test_moeh.py             | 126 +++++++++++++++++++++++
 7 files changed, 277 insertions(+), 20 deletions(-)
 create mode 100644 iohblade/methods/moeh/__init__.py
 create mode 100644 tests/unit/test_moeh.py

diff --git a/iohblade/methods/__init__.py b/iohblade/methods/__init__.py
index d4c965b..fec0607 100644
--- a/iohblade/methods/__init__.py
+++ b/iohblade/methods/__init__.py
@@ -3,6 +3,7 @@
 from .random_search import RandomSearch
 from .lhns import LHNS_Method
 from .mcts_ahd import MCTS_Method
+from .moeh import MoEH_Method, MutationType
 
 try:
     from .eoh import EoH
@@ -20,4 +21,6 @@
     "ReEvo",
     "LHNS_Method",
     "MCTS_Method",
+    "MoEH_Method",
+    "MutationType"
 ]
diff --git a/iohblade/methods/moeh/__init__.py b/iohblade/methods/moeh/__init__.py
new file mode 100644
index 0000000..4e42a0b
--- /dev/null
+++ b/iohblade/methods/moeh/__init__.py
@@ -0,0 +1,3 @@
+from iohblade.methods.moeh.moeh import MoEH_Method, MutationType
+
+__all__ = ['MoEH_Method', 'MutationType']
\ No newline at end of file
diff --git a/iohblade/methods/moeh/moeh.py b/iohblade/methods/moeh/moeh.py
index b1d7c29..a09aa4b 100644
--- a/iohblade/methods/moeh/moeh.py
+++ b/iohblade/methods/moeh/moeh.py
@@ -1,6 +1,7 @@
 import random
-import numpy as np
+import inspect
 from enum import Enum
+from typing import Any
 
 from iohblade.llm import LLM
 from iohblade.method import Method
@@ -50,11 +51,11 @@ def __init__(self,
         
         assert max_sample_nums > 1, "Max sample number must be positive integer."
         assert type(max_sample_nums) == int, "Max sample number must be positive integer."
-        assert population_size > 1, "Population size must be positive integer."
+        assert population_size >= 1, "Population size must be positive integer."
         assert type(population_size) == int, "Population size must be positive integer."
-        assert iterations > 1, "Iteration count must be positive integer."
+        assert iterations >= 1, "Iteration count must be positive integer."
         assert type(iterations) == int, "Iteration count must be positive integer."
-        assert max_workers > 1, "Max Workers must be positive integer."
+        assert max_workers >= 1, "Max Workers must be positive integer."
         assert type(max_workers) == int, "Max Workers must be positive integer."
 
         # Accepted parameters.
@@ -101,14 +102,21 @@ def initialise(self):
                                             self.problem.format_prompt)
         
         #TODO: Parallel this.....
-        while (len(self.population)  < self.population_size) and self.max_sample_nums > 0:
-            individual = self.llm.sample_solution([prompt])
-            individual = self.evaluate(individual)
+        while (len(self.population.population)  < self.population_size and self.max_sample_nums > 0):
+            individual = self.llm.sample_solution([
+                    {
+                        'role': 'user',
+                        'content': prompt
+                    }
+                ])
+            individual = self.get_fitness(individual)
             self.population.append(individual)
             self.max_sample_nums -= 1
     
-    def evaluate(self, individual: Solution):
-        return self.problem(individual)
+    def get_fitness(self, individual: Solution):
+        individual = self.problem(individual)
+        print(individual.fitness, individual.feedback, self.problem)
+        return individual
     
     def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
         """
@@ -134,14 +142,22 @@ def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop[0])
-                solution = self.llm.sample_solution([prompt], parent_ids=[pop[0]])
+                solution = self.llm.sample_solution([
+                    {
+                        'role': 'user',
+                        'content': prompt
+                    }], parent_ids=[pop[0]])
                 return solution
             case MutationType.M2:
                 prompt = MoEH_Prompts.get_prompt_m2(self.problem.task_prompt, 
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop[0])
-                solution = self.llm.sample_solution([prompt], parent_ids=[pop[0]])
+                solution = self.llm.sample_solution([
+                    {
+                        'role': 'user',
+                        'content': prompt
+                    }], parent_ids=[pop[0]])
                 return solution
 
             case MutationType.E2:
@@ -149,7 +165,11 @@ def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop)
-                solution = self.llm.sample_solution([prompt], parent_ids=[pop[i] for i in range(len(pop))])
+                solution = self.llm.sample_solution([
+                    {
+                        'role': 'user',
+                        'content': prompt
+                    }], parent_ids=[pop[i] for i in range(len(pop))])
                 return solution
 
     def evolve_solution(self, population_index: int) -> Solution:
@@ -160,16 +180,16 @@ def evolve_solution(self, population_index: int) -> Solution:
                 solution = self.query_individual(mutation_type, pop)
             case _:
                 solution = self.query_individual(mutation_type, [pop[population_index]])
-        solution = self.evaluate(solution)
+        solution = self.get_fitness(solution)
         self.max_sample_nums -= 1
         return solution
     
-    def run(self) -> Solution:
+    def run(self) -> list[Solution]:
         generation = 0
         new_population : list[Solution] = []
 
         self.initialise()
-        while((self.iterations < generation) and (self.max_sample_nums > 0)):
+        while((self.iterations < generation) or (self.max_sample_nums > 0)):
             new_population = []
             self.population.parent_selection(self.population_size)
             for index in range(len(self.population.population)):
@@ -180,6 +200,103 @@ def run(self) -> Solution:
                 self.population.append(individual)
             
             _ = self.population.population_management()
-        
+            print(len(self.population.population))
         return self.population.get_best()
             
+class MoEH_Method(Method):
+    def __init__(self, 
+                 llm: LLM, 
+                 budget: int,
+                 iterations: int,
+                 population_size: int,
+                 use_e2_operator: bool = True,
+                 use_m1_operator: bool = True,
+                 use_m2_operator: bool = True,
+                 name="MoEH"):
+        '''A `Multi-Objective Evolution of Heuristic Using LLLM` implementation for BLADE, as defined in paper: https://arxiv.org/pdf/2409.16867
+
+            ## Params:
+            - `llm: LLM`: Object of one of the final class in `iohblade.llm`.
+            - `problem: Problem`: Object instance of a class that inherits from `iohblade.problem`.
+            - `budget: int`: Max number sample that can be generated by the LLM.
+            - `iterations: int` Max number of iterations for which the algorithm is to be run.
+            - `population_size: int`: Population size (λ / µ) for the evolutionary algorithm.
+            - `use_e2_operator: bool(True)`: Use e2 operation as stated in the `moeh.prompts`; multi-individuals -> 1 individual mutation.
+            - `use_m1_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (adds novel methods.)
+            - `use_m2_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (used different approach.)
+            - `max_workers: int(2)`: Declaration of max `loky` workers for `joblib.Parallel`.
+
+            ## Note:
+
+            - The algorithm quits running when either of the conditons are met (||):
+                - `iterations` count of generations are iterated over.
+                - `max_sample_nums` count of samples are generated.
+        '''
+        super().__init__(llm, budget, name=name)
+        self.iterations = iterations
+        self.population_size = population_size
+        self.use_e2_operator = use_e2_operator
+        self.use_m1_operator = use_m1_operator
+        self.use_m2_operator = use_m2_operator
+        sig = inspect.signature(self.__init__)
+        self.init_params = {
+            k: getattr(self, k)
+            for k in sig.parameters
+            if k not in ("self", "name", "budget", "llm")
+        }
+
+    def __call__(self, problem: Problem) -> Solution:
+        self.moeh_instance = MoEH(
+            self.llm,
+            problem,
+            self.iterations,
+            self.budget,
+            self.population_size,
+            self.use_e2_operator,
+            self.use_m1_operator,
+            self.use_m2_operator,
+            1
+        )
+        try:
+            return self.moeh_instance.run()
+        except Exception as e:
+            import traceback
+            print("Some error occured, best solution till now V")
+            print(traceback.print_exc(), e, "-----------", sep="\n")
+            return self.moeh_instance.population.get_best()
+    
+    def to_dict(self):
+        """
+        Returns a dictionary representation of the method including all parameters.
+
+        Returns:
+            dict: Dictionary representation of the method.
+        """
+        kwargs = dict(self.init_params)
+        return {
+            "method_name": self.name if self.name != None else "MCTS_AHD",
+            "budget": self.budget,
+            "kwargs": kwargs,
+        }
+
+
+    # region Database Logger:
+    def get_config(self) -> dict[str, Any]:
+        config = {
+            "iterations" : self.iterations,
+            "max_sample_nums" : self.budget,
+            "population_size" : self.population_size,
+            "use_e2_operator" : self.use_e2_operator,
+            "use_m1_operator" : self.use_m1_operator,
+            "use_m2_operator" : self.use_m2_operator,
+            "minimisation": self.moeh_instance.minimisation,
+        }
+
+        return {
+            "name": self.name,
+            "source": "https://github.com/XAI-liacs/BLADE/tree/main/iohblade/methods/moeh",
+            "config": config,
+        }
+
+
+# endregion
\ No newline at end of file
diff --git a/iohblade/methods/moeh/population.py b/iohblade/methods/moeh/population.py
index 3af22e6..d3c0a8b 100644
--- a/iohblade/methods/moeh/population.py
+++ b/iohblade/methods/moeh/population.py
@@ -91,8 +91,14 @@ def population_management(self, test: bool = False) -> list[Solution]:
         self._population = new_population
         return self._population
     
-    def get_best(self) -> Solution:
+    def get_best(self) -> list[Solution]:
         population = list(sorted(self._population, key=lambda x: x.fitness))
         if self.minimisation:
-            return population[0]
-        return population[-1]
\ No newline at end of file
+            try:
+                return [population[0]]
+            except:
+                return []
+        try:
+            return [population[-1]]
+        except:
+            return []
\ No newline at end of file
diff --git a/iohblade/problem.py b/iohblade/problem.py
index ee015e2..7bffdcb 100644
--- a/iohblade/problem.py
+++ b/iohblade/problem.py
@@ -19,6 +19,8 @@
     "numpy>=2",
     "cloudpickle>=3.1.0,<4",
     "joblib>=1.4.2,<2",
+    "psutil",
+    "py-cpuinfo"
 ]
 
 import copy
diff --git a/pyproject.toml b/pyproject.toml
index fdbee67..39be181 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,7 +30,7 @@ dependencies = [
     "smac>=2.3.1",
     "virtualenv>=20.34.0",
     "feedparser>=6.0.12",
-    "py-cpuinfo"
+    "py-cpuinfo",
 ]
 
 [project.urls]
diff --git a/tests/unit/test_moeh.py b/tests/unit/test_moeh.py
new file mode 100644
index 0000000..a66ea82
--- /dev/null
+++ b/tests/unit/test_moeh.py
@@ -0,0 +1,126 @@
+from typing import Any
+import numpy as np
+
+from iohblade.llm import Dummy_LLM
+from iohblade.methods.moeh.moeh import MoEH_Method
+from iohblade.methods.moeh.moeh import MutationType
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+
+
+class DummyProblem(Problem):
+    def __init__(self, logger=None, training_instances=None, test_instances=None, name="Problem", eval_timeout=6000, dependencies=None, imports=None):
+        super().__init__(logger, training_instances, test_instances, name, eval_timeout, dependencies, imports)
+    
+    def evaluate(self, solution: Solution):
+        solution.set_scores(float('nan'), 'Invalid solution for invalid problem.')
+        return solution
+    
+    def test(self, solution: Solution):
+        return self.evaluate(solution)
+    
+    def to_dict(self):
+        dictionary = self.__dict__.copy()
+        return dictionary
+    
+    def get_config(self) -> dict[str, Any]:
+        return {
+            'tags': 'bruh',
+            'name': 'Baka Mendo',
+            'prompt': self.task_prompt + self.example_prompt + self.format_prompt,
+            'minimisation': True,
+            'evaluator': '''    def evaluate(self, solution: Solution):
+        solution.set_scores(float('nan'), 'Invalid solution for invalid problem.')
+        return solution''',
+            'config': {}
+        }
+
+class DummyProblemWorks(Problem):
+    def __init__(self, logger=None, training_instances=None, test_instances=None, name="Problem", eval_timeout=6000, dependencies=None, imports=None):
+        super().__init__(logger, training_instances, test_instances, name, eval_timeout, dependencies, imports)
+        self.iteration = 0
+    
+    def evaluate(self, solution: Solution):
+        score = 1 / (np.e ** ((-self.iteration / 10) - 10))
+        print(score)
+        solution.set_scores(score, f'Scored {score}, best known 1.0.')
+        self.iteration += 1
+        return solution
+    
+    def test(self, solution: Solution):
+        return self.evaluate(solution)
+    
+    def to_dict(self):
+        dictionary = self.__dict__.copy()
+        return dictionary
+    
+    def get_config(self) -> dict[str, Any]:
+        return {
+            'tags': 'bruh',
+            'name': 'Baka Mendo',
+            'prompt': self.task_prompt + self.example_prompt + self.format_prompt,
+            'minimisation': True,
+            'evaluator':"""    def evaluate(self, solution: Solution):
+        score = 1 / (np.e ** ((-self.iteration / 10) - 10))
+        solution.set_scores(score, f'Scored {score}, best known 1.0.')
+        self.iteration += 1
+        return solution""",
+            'config': {}
+        }
+
+def test_initialise_ok():
+    llm = Dummy_LLM()
+    problem = DummyProblem()
+    moeh = MoEH_Method(
+        llm=llm,
+        budget=10,
+        population_size=2,
+        iterations=2,
+        use_e2_operator=True,
+        use_m1_operator=False,
+        use_m2_operator=False
+    )
+    print(type(moeh).__mro__)
+    _ = moeh(problem)
+    print(type(moeh.moeh_instance).__mro__)
+
+    assert moeh.moeh_instance.llm == llm
+    assert moeh.budget == 10
+    assert moeh.moeh_instance.population_size == 2
+    assert moeh.moeh_instance.iterations == 2
+    assert MutationType.E2 in moeh.moeh_instance.allowed_mutation_types
+    assert MutationType.M1 not in moeh.moeh_instance.allowed_mutation_types
+    assert MutationType.M2 not in moeh.moeh_instance.allowed_mutation_types
+
+def test_initialisation_fails_gracefully():
+    problem = DummyProblem()
+    llm = Dummy_LLM()
+
+    moeh = MoEH_Method(
+        llm=llm,
+        budget=10,
+        population_size=2,
+        iterations=2,
+        use_e2_operator=True,
+        use_m1_operator=True,
+        use_m2_operator=True
+    )
+
+    out = moeh(problem)
+
+    assert len(out) == 0
+
+def test_initialiation_succeeds():
+    problem = DummyProblemWorks()
+    llm = Dummy_LLM()
+
+    moeh = MoEH_Method(
+        llm,
+        10,
+        1,
+        4
+    )
+    print(problem.evaluate(Solution()))
+    solution = moeh(problem)
+
+    assert len(solution) == 1
\ No newline at end of file

From 359f765d6a5afee53dea89b6f41c493c4b605e0c Mon Sep 17 00:00:00 2001
From: Ananta Shahane <ananta.shahane@aol.com>
Date: Mon, 29 Jun 2026 13:23:17 +0200
Subject: [PATCH 4/7] Debugging cloudpickle, making unsolicited pickles.

---
 iohblade/methods/__init__.py                  |  5 +-
 iohblade/methods/moeh/__init__.py             |  3 -
 iohblade/methods/moeh_method/__init__.py      |  4 ++
 .../methods/{moeh => moeh_method}/moeh.py     | 60 +++++++++----------
 iohblade/methods/moeh_method/mutationtype.py  |  7 +++
 .../{moeh => moeh_method}/population.py       |  0
 .../methods/{moeh => moeh_method}/prompts.py  |  0
 iohblade/problem.py                           |  4 +-
 iohblade/solution.py                          |  4 +-
 tests/unit/test_moeh.py                       | 12 ++--
 tests/unit/test_population.py                 |  2 +-
 11 files changed, 51 insertions(+), 50 deletions(-)
 delete mode 100644 iohblade/methods/moeh/__init__.py
 create mode 100644 iohblade/methods/moeh_method/__init__.py
 rename iohblade/methods/{moeh => moeh_method}/moeh.py (90%)
 create mode 100644 iohblade/methods/moeh_method/mutationtype.py
 rename iohblade/methods/{moeh => moeh_method}/population.py (100%)
 rename iohblade/methods/{moeh => moeh_method}/prompts.py (100%)

diff --git a/iohblade/methods/__init__.py b/iohblade/methods/__init__.py
index fec0607..5c141a7 100644
--- a/iohblade/methods/__init__.py
+++ b/iohblade/methods/__init__.py
@@ -3,7 +3,8 @@
 from .random_search import RandomSearch
 from .lhns import LHNS_Method
 from .mcts_ahd import MCTS_Method
-from .moeh import MoEH_Method, MutationType
+from .moeh_method.moeh import MutationType
+from .moeh_method.moeh import MoEH_Method
 
 try:
     from .eoh import EoH
@@ -22,5 +23,5 @@
     "LHNS_Method",
     "MCTS_Method",
     "MoEH_Method",
-    "MutationType"
+    "MutationType",
 ]
diff --git a/iohblade/methods/moeh/__init__.py b/iohblade/methods/moeh/__init__.py
deleted file mode 100644
index 4e42a0b..0000000
--- a/iohblade/methods/moeh/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from iohblade.methods.moeh.moeh import MoEH_Method, MutationType
-
-__all__ = ['MoEH_Method', 'MutationType']
\ No newline at end of file
diff --git a/iohblade/methods/moeh_method/__init__.py b/iohblade/methods/moeh_method/__init__.py
new file mode 100644
index 0000000..b88500b
--- /dev/null
+++ b/iohblade/methods/moeh_method/__init__.py
@@ -0,0 +1,4 @@
+from .mutationtype import MutationType
+from .moeh import MoEH_Method
+
+__all__ = ['MutationType', 'MoEH_Method']
\ No newline at end of file
diff --git a/iohblade/methods/moeh/moeh.py b/iohblade/methods/moeh_method/moeh.py
similarity index 90%
rename from iohblade/methods/moeh/moeh.py
rename to iohblade/methods/moeh_method/moeh.py
index a09aa4b..eb07eb0 100644
--- a/iohblade/methods/moeh/moeh.py
+++ b/iohblade/methods/moeh_method/moeh.py
@@ -1,6 +1,5 @@
 import random
 import inspect
-from enum import Enum
 from typing import Any
 
 from iohblade.llm import LLM
@@ -8,13 +7,9 @@
 from iohblade.problem import Problem
 from iohblade.solution import Solution
 
-from iohblade.methods.moeh.prompts import MoEH_Prompts
-from iohblade.methods.moeh.population import Population
-
-class MutationType(Enum):
-    E2 = 'e2'
-    M1 = 'm1'
-    M2 = 'm2'
+from .prompts import MoEH_Prompts
+from .population import Population
+from .mutationtype import MutationType
 
 
 class MoEH:
@@ -104,20 +99,31 @@ def initialise(self):
         #TODO: Parallel this.....
         while (len(self.population.population)  < self.population_size and self.max_sample_nums > 0):
             individual = self.llm.sample_solution([
-                    {
-                        'role': 'user',
-                        'content': prompt
-                    }
-                ])
+                {
+                    'role': 'user',
+                    'content': prompt
+                }
+            ])
             individual = self.get_fitness(individual)
             self.population.append(individual)
             self.max_sample_nums -= 1
     
     def get_fitness(self, individual: Solution):
         individual = self.problem(individual)
-        print(individual.fitness, individual.feedback, self.problem)
+        print(self.__dict__)
+        print(individual.fitness, individual.feedback, individual.error, sep='\n')
         return individual
     
+    def _sample_solution(self, prompt: str, parents: list[Solution]) -> Solution:
+        chat = [
+            {
+                'role': 'user',
+                'content': prompt
+            }
+        ]
+        parent_ids = list(map(lambda x: x.id, parents))
+        return self.llm.sample_solution(chat, parent_ids)
+
     def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
         """
             Queries and returns a new LLM written solution using one of the MutationTypes.
@@ -142,22 +148,14 @@ def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop[0])
-                solution = self.llm.sample_solution([
-                    {
-                        'role': 'user',
-                        'content': prompt
-                    }], parent_ids=[pop[0]])
+                solution = self._sample_solution(prompt, [pop[0]])
                 return solution
             case MutationType.M2:
                 prompt = MoEH_Prompts.get_prompt_m2(self.problem.task_prompt, 
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop[0])
-                solution = self.llm.sample_solution([
-                    {
-                        'role': 'user',
-                        'content': prompt
-                    }], parent_ids=[pop[0]])
+                solution = self._sample_solution(prompt, [pop[0]])
                 return solution
 
             case MutationType.E2:
@@ -165,11 +163,7 @@ def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop)
-                solution = self.llm.sample_solution([
-                    {
-                        'role': 'user',
-                        'content': prompt
-                    }], parent_ids=[pop[i] for i in range(len(pop))])
+                solution = self._sample_solution(prompt, pop)
                 return solution
 
     def evolve_solution(self, population_index: int) -> Solution:
@@ -185,11 +179,10 @@ def evolve_solution(self, population_index: int) -> Solution:
         return solution
     
     def run(self) -> list[Solution]:
-        generation = 0
         new_population : list[Solution] = []
 
         self.initialise()
-        while((self.iterations < generation) or (self.max_sample_nums > 0)):
+        while((self._current_iteration < self.iterations) or (self.max_sample_nums > 0)):
             new_population = []
             self.population.parent_selection(self.population_size)
             for index in range(len(self.population.population)):
@@ -201,6 +194,7 @@ def run(self) -> list[Solution]:
             
             _ = self.population.population_management()
             print(len(self.population.population))
+            self._current_iteration += 1
         return self.population.get_best()
             
 class MoEH_Method(Method):
@@ -245,7 +239,7 @@ def __init__(self,
             if k not in ("self", "name", "budget", "llm")
         }
 
-    def __call__(self, problem: Problem) -> Solution:
+    def __call__(self, problem: Problem):
         self.moeh_instance = MoEH(
             self.llm,
             problem,
@@ -274,7 +268,7 @@ def to_dict(self):
         """
         kwargs = dict(self.init_params)
         return {
-            "method_name": self.name if self.name != None else "MCTS_AHD",
+            "method_name": self.name if self.name != None else "MoEH",
             "budget": self.budget,
             "kwargs": kwargs,
         }
diff --git a/iohblade/methods/moeh_method/mutationtype.py b/iohblade/methods/moeh_method/mutationtype.py
new file mode 100644
index 0000000..b3babee
--- /dev/null
+++ b/iohblade/methods/moeh_method/mutationtype.py
@@ -0,0 +1,7 @@
+
+from enum import Enum
+
+class MutationType(Enum):
+    E2 = 'e2'
+    M1 = 'm1'
+    M2 = 'm2'
\ No newline at end of file
diff --git a/iohblade/methods/moeh/population.py b/iohblade/methods/moeh_method/population.py
similarity index 100%
rename from iohblade/methods/moeh/population.py
rename to iohblade/methods/moeh_method/population.py
diff --git a/iohblade/methods/moeh/prompts.py b/iohblade/methods/moeh_method/prompts.py
similarity index 100%
rename from iohblade/methods/moeh/prompts.py
rename to iohblade/methods/moeh_method/prompts.py
diff --git a/iohblade/problem.py b/iohblade/problem.py
index 7bffdcb..56fdf16 100644
--- a/iohblade/problem.py
+++ b/iohblade/problem.py
@@ -19,8 +19,8 @@
     "numpy>=2",
     "cloudpickle>=3.1.0,<4",
     "joblib>=1.4.2,<2",
-    "psutil",
-    "py-cpuinfo"
+    # "psutil",
+    # "py-cpuinfo"
 ]
 
 import copy
diff --git a/iohblade/solution.py b/iohblade/solution.py
index efff49d..4f855f2 100644
--- a/iohblade/solution.py
+++ b/iohblade/solution.py
@@ -138,11 +138,11 @@ def set_scores(
                     code_lines = self.code.splitlines()
                     line_no = tb.lineno
 
-                    if 1 <= line_no <= len(code_lines):
+                    if line_no in range(1, len(code_lines) + 1):
                         code_line = code_lines[line_no - 1]
                         self.error = (
                             f"{error_type}: {error_msg}.\n"
-                            f"On line {line_no}: {code_line}.\n"
+                            f"\tOn line {line_no}: {code_line}.\n"
                         )
             except:
                 self.error = repr(error)
diff --git a/tests/unit/test_moeh.py b/tests/unit/test_moeh.py
index a66ea82..021326e 100644
--- a/tests/unit/test_moeh.py
+++ b/tests/unit/test_moeh.py
@@ -2,8 +2,7 @@
 import numpy as np
 
 from iohblade.llm import Dummy_LLM
-from iohblade.methods.moeh.moeh import MoEH_Method
-from iohblade.methods.moeh.moeh import MutationType
+from iohblade.methods import MoEH_Method, MutationType
 from iohblade.problem import Problem
 from iohblade.solution import Solution
 
@@ -80,12 +79,11 @@ def test_initialise_ok():
         use_m1_operator=False,
         use_m2_operator=False
     )
-    print(type(moeh).__mro__)
+
     _ = moeh(problem)
-    print(type(moeh.moeh_instance).__mro__)
 
-    assert moeh.moeh_instance.llm == llm
-    assert moeh.budget == 10
+    assert moeh.llm == llm
+    assert moeh.moeh_instance.max_sample_nums == 10
     assert moeh.moeh_instance.population_size == 2
     assert moeh.moeh_instance.iterations == 2
     assert MutationType.E2 in moeh.moeh_instance.allowed_mutation_types
@@ -120,7 +118,7 @@ def test_initialiation_succeeds():
         1,
         4
     )
-    print(problem.evaluate(Solution()))
+
     solution = moeh(problem)
 
     assert len(solution) == 1
\ No newline at end of file
diff --git a/tests/unit/test_population.py b/tests/unit/test_population.py
index 6914d9c..0cfb8a2 100644
--- a/tests/unit/test_population.py
+++ b/tests/unit/test_population.py
@@ -1,5 +1,5 @@
 from iohblade.solution import Solution
-from iohblade.methods.moeh.population import Population
+from iohblade.methods.moeh_method.population import Population
 
 def test_population_accepts_valid_solution():
     s = Solution()

From 1957877568fc59f7afcd2a390bb62caada6afae9 Mon Sep 17 00:00:00 2001
From: Ananta Shahane <ananta.shahane@aol.com>
Date: Tue, 30 Jun 2026 15:37:21 +0200
Subject: [PATCH 5/7] Testing..

---
 iohblade/methods/__init__.py               |  2 +-
 iohblade/methods/moeh_method/moeh.py       | 59 ++++++++++--------
 iohblade/methods/moeh_method/population.py |  6 +-
 iohblade/problem.py                        |  2 -
 tests/unit/test_moeh.py                    | 71 ++++++++++++----------
 5 files changed, 78 insertions(+), 62 deletions(-)

diff --git a/iohblade/methods/__init__.py b/iohblade/methods/__init__.py
index 5c141a7..6088031 100644
--- a/iohblade/methods/__init__.py
+++ b/iohblade/methods/__init__.py
@@ -3,7 +3,7 @@
 from .random_search import RandomSearch
 from .lhns import LHNS_Method
 from .mcts_ahd import MCTS_Method
-from .moeh_method.moeh import MutationType
+from .moeh_method.mutationtype import MutationType
 from .moeh_method.moeh import MoEH_Method
 
 try:
diff --git a/iohblade/methods/moeh_method/moeh.py b/iohblade/methods/moeh_method/moeh.py
index eb07eb0..b25c6b6 100644
--- a/iohblade/methods/moeh_method/moeh.py
+++ b/iohblade/methods/moeh_method/moeh.py
@@ -7,9 +7,9 @@
 from iohblade.problem import Problem
 from iohblade.solution import Solution
 
-from .prompts import MoEH_Prompts
-from .population import Population
-from .mutationtype import MutationType
+from iohblade.methods.moeh_method.prompts import MoEH_Prompts
+from iohblade.methods.moeh_method.population import Population
+from iohblade.methods.moeh_method.mutationtype import MutationType
 
 
 class MoEH:
@@ -19,6 +19,7 @@ def __init__(self,
                  iterations: int,
                  max_sample_nums: int,
                  population_size: int,
+                 minimisation: bool,
                  use_e2_operator: bool = True,
                  use_m1_operator: bool = True,
                  use_m2_operator: bool = True,
@@ -32,6 +33,7 @@ def __init__(self,
             - `iterations: int` Max number of iterations for which the algorithm is to be run.
             - `max_sample_nums: int`: Max number sample that can be generated by the LLM.
             - `population_size: int`: Population size (λ / µ) for the evolutionary algorithm.
+            - `minimisation: int`: Optimisation direction for the evolutionary algorithm.
             - `use_e2_operator: bool(True)`: Use e2 operation as stated in the `moeh.prompts`; multi-individuals -> 1 individual mutation.
             - `use_m1_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (adds novel methods.)
             - `use_m2_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (used different approach.)
@@ -60,11 +62,13 @@ def __init__(self,
         self.max_sample_nums = max_sample_nums
 
         self.max_sample_nums = max_sample_nums
+        self._remianing_budget = max_sample_nums
         self.population_size = population_size
         self.iterations = iterations
 
+        self.minimisation = minimisation
         config = self.problem.get_config()
-        self.minimisation = config['minimisation']
+        assert self.minimisation == config['minimisation'], 'Problem.minimisation and minimisation parameter passed must match.'
 
 
         # Runtime parameters:
@@ -97,32 +101,22 @@ def initialise(self):
                                             self.problem.format_prompt)
         
         #TODO: Parallel this.....
-        while (len(self.population.population)  < self.population_size and self.max_sample_nums > 0):
-            individual = self.llm.sample_solution([
-                {
-                    'role': 'user',
-                    'content': prompt
-                }
-            ])
+        while (len(self.population.population)  < self.population_size and self._remianing_budget > 0):
+            individual = self._sample_solution(prompt, [])
             individual = self.get_fitness(individual)
             self.population.append(individual)
-            self.max_sample_nums -= 1
+            self._remianing_budget -= 1
     
     def get_fitness(self, individual: Solution):
         individual = self.problem(individual)
-        print(self.__dict__)
-        print(individual.fitness, individual.feedback, individual.error, sep='\n')
+        print(individual.fitness, individual.feedback)
         return individual
     
     def _sample_solution(self, prompt: str, parents: list[Solution]) -> Solution:
-        chat = [
-            {
-                'role': 'user',
-                'content': prompt
-            }
-        ]
+        message = [{"role": "user", "content": prompt}]
         parent_ids = list(map(lambda x: x.id, parents))
-        return self.llm.sample_solution(chat, parent_ids)
+        solution = self.llm.sample_solution(message, parent_ids)
+        return solution
 
     def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
         """
@@ -203,6 +197,7 @@ def __init__(self,
                  budget: int,
                  iterations: int,
                  population_size: int,
+                 minimisation: bool,
                  use_e2_operator: bool = True,
                  use_m1_operator: bool = True,
                  use_m2_operator: bool = True,
@@ -232,6 +227,7 @@ def __init__(self,
         self.use_e2_operator = use_e2_operator
         self.use_m1_operator = use_m1_operator
         self.use_m2_operator = use_m2_operator
+        self.minimisation = minimisation
         sig = inspect.signature(self.__init__)
         self.init_params = {
             k: getattr(self, k)
@@ -246,6 +242,7 @@ def __call__(self, problem: Problem):
             self.iterations,
             self.budget,
             self.population_size,
+            self.minimisation,
             self.use_e2_operator,
             self.use_m1_operator,
             self.use_m2_operator,
@@ -254,9 +251,9 @@ def __call__(self, problem: Problem):
         try:
             return self.moeh_instance.run()
         except Exception as e:
-            import traceback
+            # import traceback
             print("Some error occured, best solution till now V")
-            print(traceback.print_exc(), e, "-----------", sep="\n")
+            # print(traceback.print_exc(), e, "-----------", sep="\n")
             return self.moeh_instance.population.get_best()
     
     def to_dict(self):
@@ -274,7 +271,7 @@ def to_dict(self):
         }
 
 
-    # region Database Logger:
+# region Database Logger:
     def get_config(self) -> dict[str, Any]:
         config = {
             "iterations" : self.iterations,
@@ -283,7 +280,7 @@ def get_config(self) -> dict[str, Any]:
             "use_e2_operator" : self.use_e2_operator,
             "use_m1_operator" : self.use_m1_operator,
             "use_m2_operator" : self.use_m2_operator,
-            "minimisation": self.moeh_instance.minimisation,
+            "minimisation": self.minimisation,
         }
 
         return {
@@ -291,6 +288,16 @@ def get_config(self) -> dict[str, Any]:
             "source": "https://github.com/XAI-liacs/BLADE/tree/main/iohblade/methods/moeh",
             "config": config,
         }
+# endregion
+
+if __name__ == '__main__':
+    from iohblade.benchmarks.packing.circles import CirclePacking
+    from iohblade.llm import Dummy_LLM
 
+    problem = CirclePacking()
+    llm = Dummy_LLM()
+    moeh = MoEH_Method(llm, 10, 10, 2, False)
 
-# endregion
\ No newline at end of file
+    for key, value in moeh.get_config().items():
+        print(f"==========================={key}===========================")
+        print(value)
diff --git a/iohblade/methods/moeh_method/population.py b/iohblade/methods/moeh_method/population.py
index d3c0a8b..ce11bc0 100644
--- a/iohblade/methods/moeh_method/population.py
+++ b/iohblade/methods/moeh_method/population.py
@@ -27,7 +27,8 @@ def population(self):
         return self._population
 
     def parent_selection(self, d: int, test: bool=False) -> list[Solution]:
-        N = self.size
+        N = min(self.size, len(self._population))
+        print(f'Parent Selection size: {N}.')
         S = np.zeros((N, N), dtype=float)
 
         for i in range(N):
@@ -58,7 +59,10 @@ def parent_selection(self, d: int, test: bool=False) -> list[Solution]:
         return [self._population[i] for i in idx]
     
     def population_management(self, test: bool = False) -> list[Solution]:
+        # if len(self._population):
+        #     return []
         S = len(self._population)
+        print(f'Population Management size: {S}')
         M = np.zeros((S, S), dtype=float)
         for i in range(S):
             for j in range(S):
diff --git a/iohblade/problem.py b/iohblade/problem.py
index 56fdf16..ee015e2 100644
--- a/iohblade/problem.py
+++ b/iohblade/problem.py
@@ -19,8 +19,6 @@
     "numpy>=2",
     "cloudpickle>=3.1.0,<4",
     "joblib>=1.4.2,<2",
-    # "psutil",
-    # "py-cpuinfo"
 ]
 
 import copy
diff --git a/tests/unit/test_moeh.py b/tests/unit/test_moeh.py
index 021326e..caffad6 100644
--- a/tests/unit/test_moeh.py
+++ b/tests/unit/test_moeh.py
@@ -1,32 +1,32 @@
-from typing import Any
 import numpy as np
+from typing import Any
 
 from iohblade.llm import Dummy_LLM
-from iohblade.methods import MoEH_Method, MutationType
-from iohblade.problem import Problem
 from iohblade.solution import Solution
-
+from iohblade.methods import MoEH, MoEH_Method
+from iohblade.problem import Problem
 
 class DummyProblem(Problem):
-    def __init__(self, logger=None, training_instances=None, test_instances=None, name="Problem", eval_timeout=6000, dependencies=None, imports=None):
-        super().__init__(logger, training_instances, test_instances, name, eval_timeout, dependencies, imports)
-    
     def evaluate(self, solution: Solution):
         solution.set_scores(float('nan'), 'Invalid solution for invalid problem.')
         return solution
     
+    def __call__(self, solution: Solution, logger=None) -> Solution:
+            solution = self.evaluate(solution)
+            return solution
+    
     def test(self, solution: Solution):
         return self.evaluate(solution)
     
     def to_dict(self):
         dictionary = self.__dict__.copy()
         return dictionary
-    
+
     def get_config(self) -> dict[str, Any]:
         return {
             'tags': 'bruh',
             'name': 'Baka Mendo',
-            'prompt': self.task_prompt + self.example_prompt + self.format_prompt,
+            'prompt': self.get_prompt(),
             'minimisation': True,
             'evaluator': '''    def evaluate(self, solution: Solution):
         solution.set_scores(float('nan'), 'Invalid solution for invalid problem.')
@@ -35,17 +35,20 @@ def get_config(self) -> dict[str, Any]:
         }
 
 class DummyProblemWorks(Problem):
-    def __init__(self, logger=None, training_instances=None, test_instances=None, name="Problem", eval_timeout=6000, dependencies=None, imports=None):
-        super().__init__(logger, training_instances, test_instances, name, eval_timeout, dependencies, imports)
+    def __init__(self):
+        super().__init__()
         self.iteration = 0
-    
+
     def evaluate(self, solution: Solution):
-        score = 1 / (np.e ** ((-self.iteration / 10) - 10))
-        print(score)
+        score = 1 / (1 + np.e ** ((-self.iteration / 10) - 10))
         solution.set_scores(score, f'Scored {score}, best known 1.0.')
         self.iteration += 1
         return solution
     
+    def __call__(self, solution: Solution, logger=None) -> Solution:
+            solution = self.evaluate(solution)
+            return solution
+    
     def test(self, solution: Solution):
         return self.evaluate(solution)
     
@@ -57,7 +60,7 @@ def get_config(self) -> dict[str, Any]:
         return {
             'tags': 'bruh',
             'name': 'Baka Mendo',
-            'prompt': self.task_prompt + self.example_prompt + self.format_prompt,
+            'prompt': self.get_prompt(),
             'minimisation': True,
             'evaluator':"""    def evaluate(self, solution: Solution):
         score = 1 / (np.e ** ((-self.iteration / 10) - 10))
@@ -69,7 +72,6 @@ def get_config(self) -> dict[str, Any]:
 
 def test_initialise_ok():
     llm = Dummy_LLM()
-    problem = DummyProblem()
     moeh = MoEH_Method(
         llm=llm,
         budget=10,
@@ -77,34 +79,38 @@ def test_initialise_ok():
         iterations=2,
         use_e2_operator=True,
         use_m1_operator=False,
-        use_m2_operator=False
+        use_m2_operator=False,
+        minimisation=True
     )
 
-    _ = moeh(problem)
-
     assert moeh.llm == llm
-    assert moeh.moeh_instance.max_sample_nums == 10
-    assert moeh.moeh_instance.population_size == 2
-    assert moeh.moeh_instance.iterations == 2
-    assert MutationType.E2 in moeh.moeh_instance.allowed_mutation_types
-    assert MutationType.M1 not in moeh.moeh_instance.allowed_mutation_types
-    assert MutationType.M2 not in moeh.moeh_instance.allowed_mutation_types
+    assert moeh.budget == 10
+    assert moeh.population_size == 2
+    assert moeh.iterations == 2
+    assert moeh.use_e2_operator == True
+    assert moeh.use_m1_operator == False
+    assert moeh.use_m2_operator == False
+    assert moeh.minimisation == True
 
 def test_initialisation_fails_gracefully():
     problem = DummyProblem()
     llm = Dummy_LLM()
 
-    moeh = MoEH_Method(
+    moeh = MoEH(
         llm=llm,
-        budget=10,
+        problem=problem,
+        max_sample_nums=10,
         population_size=2,
         iterations=2,
         use_e2_operator=True,
         use_m1_operator=True,
-        use_m2_operator=True
+        use_m2_operator=True,
+        minimisation=True
     )
 
-    out = moeh(problem)
+
+
+    out = moeh.run()
 
     assert len(out) == 0
 
@@ -114,9 +120,10 @@ def test_initialiation_succeeds():
 
     moeh = MoEH_Method(
         llm,
-        10,
-        1,
-        4
+        20,
+        2,
+        4,
+        True
     )
 
     solution = moeh(problem)

From d84d49245d9910ce9c3e367df3af6c075d6affc4 Mon Sep 17 00:00:00 2001
From: Ananta Shahane <ananta.shahane@aol.com>
Date: Thu, 2 Jul 2026 11:01:47 +0200
Subject: [PATCH 6/7] Testing Population.

---
 iohblade/methods/moeh_method/moeh.py       |  14 +-
 iohblade/methods/moeh_method/population.py |  47 ++--
 iohblade/methods/moeh_method/prompts.py    |  13 +-
 tests/unit/test_moeh.py                    | 287 ++++++++++++++++++++-
 4 files changed, 319 insertions(+), 42 deletions(-)

diff --git a/iohblade/methods/moeh_method/moeh.py b/iohblade/methods/moeh_method/moeh.py
index b25c6b6..cf9feab 100644
--- a/iohblade/methods/moeh_method/moeh.py
+++ b/iohblade/methods/moeh_method/moeh.py
@@ -138,14 +138,13 @@ def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
         match(type):
             case MutationType.M1:
                 prompt = MoEH_Prompts.get_prompt_m1(
-                    self.problem.task_prompt, 
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop[0])
                 solution = self._sample_solution(prompt, [pop[0]])
                 return solution
             case MutationType.M2:
-                prompt = MoEH_Prompts.get_prompt_m2(self.problem.task_prompt, 
+                prompt = MoEH_Prompts.get_prompt_m2( 
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop[0])
@@ -153,7 +152,7 @@ def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
                 return solution
 
             case MutationType.E2:
-                prompt = MoEH_Prompts.get_prompt_e2(self.problem.task_prompt, 
+                prompt = MoEH_Prompts.get_prompt_e2(
                     self.problem.example_prompt, 
                     self.problem.format_prompt, 
                     pop)
@@ -175,8 +174,11 @@ def evolve_solution(self, population_index: int) -> Solution:
     def run(self) -> list[Solution]:
         new_population : list[Solution] = []
 
+        print(f'Started MoEH with {len(self.population.population)} individuals.')
+        
         self.initialise()
-        while((self._current_iteration < self.iterations) or (self.max_sample_nums > 0)):
+        while((self._current_iteration < self.iterations) and (self.max_sample_nums > 0)):
+            print(f'Generation {self._current_iteration} / {self.iterations}: {len(self.population.population)} individuals. Attempts remaining {self.max_sample_nums}.')
             new_population = []
             self.population.parent_selection(self.population_size)
             for index in range(len(self.population.population)):
@@ -251,9 +253,9 @@ def __call__(self, problem: Problem):
         try:
             return self.moeh_instance.run()
         except Exception as e:
-            # import traceback
+            import traceback
             print("Some error occured, best solution till now V")
-            # print(traceback.print_exc(), e, "-----------", sep="\n")
+            print(traceback.print_exc(), e, "-----------", sep="\n")
             return self.moeh_instance.population.get_best()
     
     def to_dict(self):
diff --git a/iohblade/methods/moeh_method/population.py b/iohblade/methods/moeh_method/population.py
index ce11bc0..b296af3 100644
--- a/iohblade/methods/moeh_method/population.py
+++ b/iohblade/methods/moeh_method/population.py
@@ -26,9 +26,8 @@ def __setitem__(self, key, value):
     def population(self):
         return self._population
 
-    def parent_selection(self, d: int, test: bool=False) -> list[Solution]:
-        N = min(self.size, len(self._population))
-        print(f'Parent Selection size: {N}.')
+    def _get_delta_domination_matrix(self) -> np.ndarray:
+        N = len(self._population)
         S = np.zeros((N, N), dtype=float)
 
         for i in range(N):
@@ -44,38 +43,42 @@ def parent_selection(self, d: int, test: bool=False) -> list[Solution]:
                         S[i, j] = 0
                     elif self._population[i].fitness > self._population[j].fitness and self.minimisation:
                         S[i, j] = 0
+        return S
+
 
+    def parent_selection(self, d: int, test: bool=False) -> list[Solution]:
+        print(f'Parent Selection size: {len(self._population)}.')
+        S = self._get_delta_domination_matrix()
+        if test:
+            print(f"\tS pre column sum: \n{S}")
+        if S.shape == (0, 0):
+            if test:
+                print('\tEarly exit, population empty.')
+            return []
+        
         v = np.sum(S, axis=0)
+
         if test:
-            print(f"S: {S}")
+            print(f"\tS after column sum: {S}")
+        
         v = v - np.max(v)
+
         if test:
             print(f'v: {v}')
         pdf = np.exp(v)
         pdf = pdf / pdf.sum()
         if test:
-            print(f'Softmax: {pdf}')
-        idx = np.random.choice(N, size=d, replace=False, p=pdf)
-        return [self._population[i] for i in idx]
+            print(f'\tSoftmax: {pdf}')
+        idx = np.random.choice(range(len(self._population)), size=d, replace=False, p=pdf)
+        population = [self._population[i] for i in idx]
+        self._population = population
+        return population
     
     def population_management(self, test: bool = False) -> list[Solution]:
         # if len(self._population):
         #     return []
-        S = len(self._population)
-        print(f'Population Management size: {S}')
-        M = np.zeros((S, S), dtype=float)
-        for i in range(S):
-            for j in range(S):
-                if i != j:
-                    M[i][j] = -calc_syntax_match(
-                        self._population[i].code,
-                        self._population[j].code,
-                        'python'
-                    )
-                if self._population[i].fitness < self._population[j].fitness and not self.minimisation:
-                    M[i][j] = 0
-                elif self._population[i].fitness > self._population[j].fitness and self.minimisation:
-                    M[i][j] = 0
+        M = self._get_delta_domination_matrix()
+
         if test:
             print('Selection Matrix:')
             print(M)
diff --git a/iohblade/methods/moeh_method/prompts.py b/iohblade/methods/moeh_method/prompts.py
index 05a8848..255ab11 100644
--- a/iohblade/methods/moeh_method/prompts.py
+++ b/iohblade/methods/moeh_method/prompts.py
@@ -42,7 +42,7 @@ def get_prompt_i1(cls, task_prompt, example_prompt, format_prompt) -> str:
 
     @classmethod
     def get_prompt_e2(
-        cls, task_prompt, example_prompt, format_prompt, indivs: list[Solution]
+        cls, example_prompt, format_prompt, indivs: list[Solution]
     ) -> str:
         prompt_indiv = "\n".join(
             list(
@@ -69,8 +69,7 @@ def get_prompt_e2(
             + f"Please create a new algorithm that has a similar form to the No.{len(indivs)} algorithm and is inspired by the No.{1} algorithm. The new algorithm should have a objective value lower than both algorithms.\n"
             f"Firstly, list the common ideas in the No.{1} algorithm that may give good performances. Secondly, based on the common idea, describe the design idea based on the No.{len(indivs)} algorithm and main steps of your algorithm in one sentence. \
 The description must be inside a brace. Thirdly, reply with a response adhering to following contract. Make sure only the final code is in code block. \
-'"
-            + task_prompt
+"
             + example_prompt
             + format_prompt
         )
@@ -78,7 +77,7 @@ def get_prompt_e2(
 
     @classmethod
     def get_prompt_m1(
-        cls, task_prompt, example_prompt, format_prompt, indiv: Solution
+        cls, example_prompt, format_prompt, indiv: Solution
     ) -> str:
         prompt_content = (
             "I have one algorithm with its code as follows. \n\n\
@@ -92,12 +91,12 @@ def get_prompt_m1(
 Please create a new algorithm that is a modified version of the provided algorithm. Attempt to introduce more novel mechanisms and new equations or programme segments.\n"
             "Respond in adherance to following contract:"
         )
-        prompt_content += "\n".join([task_prompt, example_prompt, format_prompt])
+        prompt_content += "\n".join([example_prompt, format_prompt])
         return prompt_content
 
     @classmethod
     def get_prompt_m2(
-        cls, task_prompt: str, example_prompt: str, format_prompt: str, indiv: Solution
+        cls, example_prompt: str, format_prompt: str, indiv: Solution
     ) -> str:
         prompt_content = (
             "I have one algorithm with its code as follows. \n\n\
@@ -111,5 +110,5 @@ def get_prompt_m2(
 Please identify the main algorithm parameters and help me in creating a new algorithm that has different parameter settings to equations compared to the provided algorithm. \n"
             "Respond in adherance to following contract:"
         )
-        prompt_content += "\n".join([task_prompt, example_prompt, format_prompt])
+        prompt_content += "\n".join([example_prompt, format_prompt])
         return prompt_content
diff --git a/tests/unit/test_moeh.py b/tests/unit/test_moeh.py
index caffad6..dc8c180 100644
--- a/tests/unit/test_moeh.py
+++ b/tests/unit/test_moeh.py
@@ -1,11 +1,15 @@
 import numpy as np
+import random
 from typing import Any
 
 from iohblade.llm import Dummy_LLM
 from iohblade.solution import Solution
-from iohblade.methods import MoEH, MoEH_Method
+from iohblade.methods import MoEH_Method
 from iohblade.problem import Problem
 
+from iohblade.methods.moeh_method.prompts import MoEH_Prompts
+
+#region Helper Classes.
 class DummyProblem(Problem):
     def evaluate(self, solution: Solution):
         solution.set_scores(float('nan'), 'Invalid solution for invalid problem.')
@@ -33,14 +37,14 @@ def get_config(self) -> dict[str, Any]:
         return solution''',
             'config': {}
         }
-
+    
 class DummyProblemWorks(Problem):
     def __init__(self):
         super().__init__()
         self.iteration = 0
 
     def evaluate(self, solution: Solution):
-        score = 1 / (1 + np.e ** ((-self.iteration / 10) - 10))
+        score = 1 / (1 + np.e ** ((-self.iteration / 4) + 10)) * random.random()
         solution.set_scores(score, f'Scored {score}, best known 1.0.')
         self.iteration += 1
         return solution
@@ -69,6 +73,276 @@ def get_config(self) -> dict[str, Any]:
         return solution""",
             'config': {}
         }
+#endregion
+
+#region Prompt:
+    
+def test_i1_prompt():
+    task_prompt = 'Write a simple black box optimiser class, that takes in a function, and optimises it. Tha class must contain __call__ method, that is in budget, and returns best result on execution.'
+    example_prompt = """
+class Optimiser:
+    def __init__(self, f):
+        self.f = f
+    
+    def __call__(self, budget = 5000) -> Solution:
+        self.budget = budget
+        self.initialise()
+        self.evaluate()
+        while(not self.budget_exhauseted()):
+            self.evolve_population()
+            self.evaluate()
+            self.selection()
+        return self.best
+"""
+
+    output_prompt = """
+Always replay in following format:
+# Description : <short description of the approach.>
+# Code:
+```python
+<code>
+```
+"""
+
+    prompt = MoEH_Prompts.get_prompt_i1(task_prompt, example_prompt, output_prompt)
+    assert prompt == task_prompt + '\n' + example_prompt + '\n' + output_prompt
+
+def test_e2_prompt():
+    solution = Solution()
+    solution.code = '''
+class BBOBSolver:
+    def __init__(self, func, budget, dim, rng=None):
+        """
+        Parameters
+        ----------
+        func : callable
+            Objective function taking a 1D numpy array.
+        budget : int
+            Maximum number of function evaluations.
+        dim : int
+            Problem dimension.
+        rng : np.random.Generator, optional
+            Random number generator.
+        """
+        self.func = func
+        self.budget = budget
+        self.dim = dim
+        self.rng = rng or np.random.default_rng()
+
+        self.best_x = None
+        self.best_f = np.inf
+        self.evaluations = 0
+
+    def ask(self):
+        """
+        Generate a candidate solution.
+        Override this method for your own algorithm.
+        """
+        return self.rng.uniform(-5, 5, self.dim)
+
+    def tell(self, x, fx):
+        """
+        Update the algorithm state.
+        Override this method if needed.
+        """
+        if fx < self.best_f:
+            self.best_f = fx
+            self.best_x = x.copy()
+
+    def run(self):
+        """
+        Run the optimization until the evaluation budget is exhausted.
+
+        Returns
+        -------
+        best_x : ndarray
+            Best solution found.
+        best_f : float
+            Objective value of the best solution.
+        """
+        while self.evaluations < self.budget:
+            x = self.ask()
+            fx = self.func(x)
+
+            self.evaluations += 1
+            self.tell(x, fx)
+
+        return self.best_x, self.best_f'''
+    solution.description = 'a minimal Python template for optimizing a BBOB function. It accepts a function and an evaluation budget, provides a run() method, and returns the best solution found.'
+
+    solution2 = Solution()
+    solution2.code = '''
+class Solver:
+    def __init__(self, func, budget, dim):
+        self.func = func
+        self.budget = budget
+        self.dim = dim
+
+    def run(self):
+        best_x, best_f = None, float("inf")
+
+        for _ in range(self.budget):
+            x = np.random.uniform(-5, 5, self.dim)
+            f = self.func(x)
+
+            if f < best_f:
+                best_x, best_f = x, f
+
+        return best_x, best_f
+'''
+
+    solution2.description = 'A simple random search optimiser for the bbob problem.'
+
+    solution.set_scores(
+        0.83,
+        'Scored 0.83, higher is better.'
+    )
+
+    solution2.set_scores(
+        0.12,
+        'Scored 0.12, higher is better.'
+    )
+
+    example_prompt = """
+class Optimiser:
+    def __init__(self, f):
+        self.f = f
+    
+    def __call__(self, budget = 5000) -> Solution:
+        self.budget = budget
+        self.initialise()
+        self.evaluate()
+        while(not self.budget_exhauseted()):
+            self.evolve_population()
+            self.evaluate()
+            self.selection()
+        return self.best
+"""
+
+    output_prompt = """
+Always replay in following format:
+# Description : <short description of the approach.>
+# Code:
+```python
+<code>
+```
+"""
+    prompt = MoEH_Prompts.get_prompt_e2(example_prompt, output_prompt, [solution, solution2])
+
+    assert solution.code in prompt
+    assert solution2.code in prompt
+
+    assert solution.description in prompt
+    assert solution2.description in prompt
+
+    assert example_prompt in prompt
+    assert output_prompt in prompt
+
+def test_m_prompts():
+    solution = Solution()
+    solution.code = '''
+class BBOBSolver:
+    def __init__(self, func, budget, dim, rng=None):
+        """
+        Parameters
+        ----------
+        func : callable
+            Objective function taking a 1D numpy array.
+        budget : int
+            Maximum number of function evaluations.
+        dim : int
+            Problem dimension.
+        rng : np.random.Generator, optional
+            Random number generator.
+        """
+        self.func = func
+        self.budget = budget
+        self.dim = dim
+        self.rng = rng or np.random.default_rng()
+
+        self.best_x = None
+        self.best_f = np.inf
+        self.evaluations = 0
+
+    def ask(self):
+        """
+        Generate a candidate solution.
+        Override this method for your own algorithm.
+        """
+        return self.rng.uniform(-5, 5, self.dim)
+
+    def tell(self, x, fx):
+        """
+        Update the algorithm state.
+        Override this method if needed.
+        """
+        if fx < self.best_f:
+            self.best_f = fx
+            self.best_x = x.copy()
+
+    def run(self):
+        """
+        Run the optimization until the evaluation budget is exhausted.
+
+        Returns
+        -------
+        best_x : ndarray
+            Best solution found.
+        best_f : float
+            Objective value of the best solution.
+        """
+        while self.evaluations < self.budget:
+            x = self.ask()
+            fx = self.func(x)
+
+            self.evaluations += 1
+            self.tell(x, fx)
+
+        return self.best_x, self.best_f'''
+    solution.description = 'A minimal Python template for optimizing a BBOB function. It accepts a function and an evaluation budget, provides a run() method, and returns the best solution found.'
+    
+    example_prompt = """
+class Optimiser:
+    def __init__(self, f):
+        self.f = f
+    
+    def __call__(self, budget = 5000) -> Solution:
+        self.budget = budget
+        self.initialise()
+        self.evaluate()
+        while(not self.budget_exhauseted()):
+            self.evolve_population()
+            self.evaluate()
+            self.selection()
+        return self.best
+"""
+
+    output_prompt = """
+Always replay in following format:
+# Description : <short description of the approach.>
+# Code:
+```python
+<code>
+```
+"""
+    prompt = MoEH_Prompts.get_prompt_m1(example_prompt, output_prompt, solution)
+    assert solution.code in prompt
+    assert solution.description in prompt
+    assert output_prompt in prompt
+    assert example_prompt in prompt
+    assert "modified version of the provided algorithm" in prompt #M1 is considered mutation.
+
+    prompt = MoEH_Prompts.get_prompt_m2(example_prompt, output_prompt, solution)
+    assert solution.code in prompt
+    assert solution.description in prompt
+    assert output_prompt in prompt
+    assert example_prompt in prompt
+    assert " a new algorithm that has different parameter settings" in prompt #M1 is considered as hyper parameter optimisation.
+#endregion
+
+#region Population
+
+#endregion
 
 def test_initialise_ok():
     llm = Dummy_LLM()
@@ -96,10 +370,9 @@ def test_initialisation_fails_gracefully():
     problem = DummyProblem()
     llm = Dummy_LLM()
 
-    moeh = MoEH(
+    moeh = MoEH_Method(
         llm=llm,
-        problem=problem,
-        max_sample_nums=10,
+        budget=10,
         population_size=2,
         iterations=2,
         use_e2_operator=True,
@@ -110,7 +383,7 @@ def test_initialisation_fails_gracefully():
 
 
 
-    out = moeh.run()
+    out = moeh(problem)
 
     assert len(out) == 0
 

From 5c585951b307eb7807418f7a031f9dce4a3f0f40 Mon Sep 17 00:00:00 2001
From: Ananta Shahane <ananta.shahane@aol.com>
Date: Thu, 2 Jul 2026 15:07:08 +0200
Subject: [PATCH 7/7] MoEH.beta.

---
 iohblade/methods/moeh_method/__init__.py     |   2 +-
 iohblade/methods/moeh_method/moeh.py         | 277 ++++++++++---------
 iohblade/methods/moeh_method/mutationtype.py |   8 +-
 iohblade/methods/moeh_method/population.py   | 106 +++----
 iohblade/methods/moeh_method/prompts.py      |   4 +-
 iohblade/solution.py                         |   4 +-
 pyproject.toml                               |   1 +
 tests/unit/test_moeh.py                      | 198 +++++++++++--
 uv.lock                                      | 211 ++++++++++++++
 9 files changed, 602 insertions(+), 209 deletions(-)

diff --git a/iohblade/methods/moeh_method/__init__.py b/iohblade/methods/moeh_method/__init__.py
index b88500b..02f3069 100644
--- a/iohblade/methods/moeh_method/__init__.py
+++ b/iohblade/methods/moeh_method/__init__.py
@@ -1,4 +1,4 @@
 from .mutationtype import MutationType
 from .moeh import MoEH_Method
 
-__all__ = ['MutationType', 'MoEH_Method']
\ No newline at end of file
+__all__ = ["MutationType", "MoEH_Method"]
diff --git a/iohblade/methods/moeh_method/moeh.py b/iohblade/methods/moeh_method/moeh.py
index cf9feab..01aec5d 100644
--- a/iohblade/methods/moeh_method/moeh.py
+++ b/iohblade/methods/moeh_method/moeh.py
@@ -13,41 +13,45 @@
 
 
 class MoEH:
-    def __init__(self,
-                 llm: LLM,
-                 problem: Problem,
-                 iterations: int,
-                 max_sample_nums: int,
-                 population_size: int,
-                 minimisation: bool,
-                 use_e2_operator: bool = True,
-                 use_m1_operator: bool = True,
-                 use_m2_operator: bool = True,
-                 max_workers: int = 2) -> None:
+    def __init__(
+        self,
+        llm: LLM,
+        problem: Problem,
+        iterations: int,
+        max_sample_nums: int,
+        population_size: int,
+        minimisation: bool,
+        use_e2_operator: bool = True,
+        use_m1_operator: bool = True,
+        use_m2_operator: bool = True,
+        max_workers: int = 2,
+    ) -> None:
         """
-            A `Multi-Objective Evolution of Heuristic Using LLLM` implementation for BLADE, as defined in paper: https://arxiv.org/pdf/2409.16867
-
-            ## Params:
-            - `llm: LLM`: Object of one of the final class in `iohblade.llm`.
-            - `problem: Problem`: Object instance of a class that inherits from `iohblade.problem`.
-            - `iterations: int` Max number of iterations for which the algorithm is to be run.
-            - `max_sample_nums: int`: Max number sample that can be generated by the LLM.
-            - `population_size: int`: Population size (λ / µ) for the evolutionary algorithm.
-            - `minimisation: int`: Optimisation direction for the evolutionary algorithm.
-            - `use_e2_operator: bool(True)`: Use e2 operation as stated in the `moeh.prompts`; multi-individuals -> 1 individual mutation.
-            - `use_m1_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (adds novel methods.)
-            - `use_m2_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (used different approach.)
-            - `max_workers: int(2)`: Declaration of max `loky` workers for `joblib.Parallel`.
-
-            ## Note:
-
-            - The algorithm quits running when either of the conditons are met (||):
-                - `iterations` count of generations are iterated over.
-                - `max_sample_nums` count of samples are generated.
+        A `Multi-Objective Evolution of Heuristic Using LLLM` implementation for BLADE, as defined in paper: https://arxiv.org/pdf/2409.16867
+
+        ## Params:
+        - `llm: LLM`: Object of one of the final class in `iohblade.llm`.
+        - `problem: Problem`: Object instance of a class that inherits from `iohblade.problem`.
+        - `iterations: int` Max number of iterations for which the algorithm is to be run.
+        - `max_sample_nums: int`: Max number sample that can be generated by the LLM.
+        - `population_size: int`: Population size (λ / µ) for the evolutionary algorithm.
+        - `minimisation: int`: Optimisation direction for the evolutionary algorithm.
+        - `use_e2_operator: bool(True)`: Use e2 operation as stated in the `moeh.prompts`; multi-individuals -> 1 individual mutation.
+        - `use_m1_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (adds novel methods.)
+        - `use_m2_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (used different approach.)
+        - `max_workers: int(2)`: Declaration of max `loky` workers for `joblib.Parallel`.
+
+        ## Note:
+
+        - The algorithm quits running when either of the conditons are met (||):
+            - `iterations` count of generations are iterated over.
+            - `max_sample_nums` count of samples are generated.
         """
-        
+
         assert max_sample_nums > 1, "Max sample number must be positive integer."
-        assert type(max_sample_nums) == int, "Max sample number must be positive integer."
+        assert (
+            type(max_sample_nums) == int
+        ), "Max sample number must be positive integer."
         assert population_size >= 1, "Population size must be positive integer."
         assert type(population_size) == int, "Population size must be positive integer."
         assert iterations >= 1, "Iteration count must be positive integer."
@@ -68,8 +72,9 @@ def __init__(self,
 
         self.minimisation = minimisation
         config = self.problem.get_config()
-        assert self.minimisation == config['minimisation'], 'Problem.minimisation and minimisation parameter passed must match.'
-
+        assert (
+            self.minimisation == config["minimisation"]
+        ), "Problem.minimisation and minimisation parameter passed must match."
 
         # Runtime parameters:
         self._current_iteration = 0
@@ -84,34 +89,40 @@ def __init__(self,
         if use_m2_operator:
             self.allowed_mutation_types.append(MutationType.M2)
 
-        assert len(self.allowed_mutation_types) >= 1, "Atleast one mutation type is expected, use_x_operator all set false in invalid state."
+        assert (
+            len(self.allowed_mutation_types) >= 1
+        ), "Atleast one mutation type is expected, use_x_operator all set false in invalid state."
 
     def initialise(self):
         """
-            Initialises the evolutionary algorithm.
+        Initialises the evolutionary algorithm.
 
-            # Params:
-                - `None`
+        # Params:
+            - `None`
 
-            # Returns:
-                - Inline function, doesn't return anything, updates `self.population`.
+        # Returns:
+            - Inline function, doesn't return anything, updates `self.population`.
         """
-        prompt = MoEH_Prompts.get_prompt_i1(self.problem.task_prompt, 
-                                            self.problem.example_prompt, 
-                                            self.problem.format_prompt)
-        
-        #TODO: Parallel this.....
-        while (len(self.population.population)  < self.population_size and self._remianing_budget > 0):
+        prompt = MoEH_Prompts.get_prompt_i1(
+            self.problem.task_prompt,
+            self.problem.example_prompt,
+            self.problem.format_prompt,
+        )
+
+        # TODO: Parallel this.....
+        while (
+            len(self.population.population) < self.population_size
+            and self._remianing_budget > 0
+        ):
             individual = self._sample_solution(prompt, [])
             individual = self.get_fitness(individual)
             self.population.append(individual)
             self._remianing_budget -= 1
-    
+
     def get_fitness(self, individual: Solution):
         individual = self.problem(individual)
-        print(individual.fitness, individual.feedback)
         return individual
-    
+
     def _sample_solution(self, prompt: str, parents: list[Solution]) -> Solution:
         message = [{"role": "user", "content": prompt}]
         parent_ids = list(map(lambda x: x.id, parents))
@@ -120,49 +131,45 @@ def _sample_solution(self, prompt: str, parents: list[Solution]) -> Solution:
 
     def query_individual(self, type: MutationType, pop: list[Solution]) -> Solution:
         """
-            Queries and returns a new LLM written solution using one of the MutationTypes.
-        
-            ## Parameters:
-            - `type: MutationType`: Expects an enum typed `MutationType` to decide which prompt to use for an evolution.
-            - `pop: list[Solution]`: Expects an array of solution of size 1 for `M1` and `M2` types, and of arbitrary size [1, n] for `E2`.
-
-            ## Expectations:
-            - For each `MutationType` following size of population is expected:
-                - `M1`: 1
-                - `M2`: 1
-                - `E2`: [1, n]
-
-            ## Returns:
-            - `Solution`: an evolved solution generated by LLM using given pop and `MutationType`.
+        Queries and returns a new LLM written solution using one of the MutationTypes.
+
+        ## Parameters:
+        - `type: MutationType`: Expects an enum typed `MutationType` to decide which prompt to use for an evolution.
+        - `pop: list[Solution]`: Expects an array of solution of size 1 for `M1` and `M2` types, and of arbitrary size [1, n] for `E2`.
+
+        ## Expectations:
+        - For each `MutationType` following size of population is expected:
+            - `M1`: 1
+            - `M2`: 1
+            - `E2`: [1, n]
+
+        ## Returns:
+        - `Solution`: an evolved solution generated by LLM using given pop and `MutationType`.
         """
-        match(type):
+        match (type):
             case MutationType.M1:
                 prompt = MoEH_Prompts.get_prompt_m1(
-                    self.problem.example_prompt, 
-                    self.problem.format_prompt, 
-                    pop[0])
+                    self.problem.example_prompt, self.problem.format_prompt, pop[0]
+                )
                 solution = self._sample_solution(prompt, [pop[0]])
                 return solution
             case MutationType.M2:
-                prompt = MoEH_Prompts.get_prompt_m2( 
-                    self.problem.example_prompt, 
-                    self.problem.format_prompt, 
-                    pop[0])
+                prompt = MoEH_Prompts.get_prompt_m2(
+                    self.problem.example_prompt, self.problem.format_prompt, pop[0]
+                )
                 solution = self._sample_solution(prompt, [pop[0]])
                 return solution
 
             case MutationType.E2:
                 prompt = MoEH_Prompts.get_prompt_e2(
-                    self.problem.example_prompt, 
-                    self.problem.format_prompt, 
-                    pop)
+                    self.problem.example_prompt, self.problem.format_prompt, pop
+                )
                 solution = self._sample_solution(prompt, pop)
                 return solution
 
-    def evolve_solution(self, population_index: int) -> Solution:
+    def evolve_solution(self, population_index: int, pop: list[Solution]) -> Solution:
         mutation_type = random.choice(self.allowed_mutation_types)
-        pop = self.population.population
-        match(mutation_type):
+        match (mutation_type):
             case MutationType.E2:
                 solution = self.query_individual(mutation_type, pop)
             case _:
@@ -170,59 +177,65 @@ def evolve_solution(self, population_index: int) -> Solution:
         solution = self.get_fitness(solution)
         self.max_sample_nums -= 1
         return solution
-    
+
     def run(self) -> list[Solution]:
-        new_population : list[Solution] = []
+        new_population: list[Solution] = []
 
-        print(f'Started MoEH with {len(self.population.population)} individuals.')
-        
         self.initialise()
-        while((self._current_iteration < self.iterations) and (self.max_sample_nums > 0)):
-            print(f'Generation {self._current_iteration} / {self.iterations}: {len(self.population.population)} individuals. Attempts remaining {self.max_sample_nums}.')
+        print(f"Started MoEH with {len(self.population._population)} individuals.")
+
+        while (self._current_iteration < self.iterations) and (
+            self.max_sample_nums > 0
+        ):
+            print(
+                f"Generation {self._current_iteration + 1} / {self.iterations}: {len(self.population.population)} individuals. Best solution = {[individual.fitness for individual in self.population.get_best()]} Attempts remaining {self.max_sample_nums}."
+            )
             new_population = []
-            self.population.parent_selection(self.population_size)
-            for index in range(len(self.population.population)):
-                new_individual = self.evolve_solution(index)
+            old_population = self.population.parent_selection(self.population_size)
+            for index in range(len(old_population)):
+                new_individual = self.evolve_solution(index, old_population)
                 new_population.append(new_individual)
-            
+
             for individual in new_population:
                 self.population.append(individual)
-            
+
             _ = self.population.population_management()
-            print(len(self.population.population))
             self._current_iteration += 1
         return self.population.get_best()
-            
+
+
 class MoEH_Method(Method):
-    def __init__(self, 
-                 llm: LLM, 
-                 budget: int,
-                 iterations: int,
-                 population_size: int,
-                 minimisation: bool,
-                 use_e2_operator: bool = True,
-                 use_m1_operator: bool = True,
-                 use_m2_operator: bool = True,
-                 name="MoEH"):
-        '''A `Multi-Objective Evolution of Heuristic Using LLLM` implementation for BLADE, as defined in paper: https://arxiv.org/pdf/2409.16867
-
-            ## Params:
-            - `llm: LLM`: Object of one of the final class in `iohblade.llm`.
-            - `problem: Problem`: Object instance of a class that inherits from `iohblade.problem`.
-            - `budget: int`: Max number sample that can be generated by the LLM.
-            - `iterations: int` Max number of iterations for which the algorithm is to be run.
-            - `population_size: int`: Population size (λ / µ) for the evolutionary algorithm.
-            - `use_e2_operator: bool(True)`: Use e2 operation as stated in the `moeh.prompts`; multi-individuals -> 1 individual mutation.
-            - `use_m1_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (adds novel methods.)
-            - `use_m2_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (used different approach.)
-            - `max_workers: int(2)`: Declaration of max `loky` workers for `joblib.Parallel`.
-
-            ## Note:
-
-            - The algorithm quits running when either of the conditons are met (||):
-                - `iterations` count of generations are iterated over.
-                - `max_sample_nums` count of samples are generated.
-        '''
+    def __init__(
+        self,
+        llm: LLM,
+        budget: int,
+        iterations: int,
+        population_size: int,
+        minimisation: bool,
+        use_e2_operator: bool = True,
+        use_m1_operator: bool = True,
+        use_m2_operator: bool = True,
+        name="MoEH",
+    ):
+        """A `Multi-Objective Evolution of Heuristic Using LLLM` implementation for BLADE, as defined in paper: https://arxiv.org/pdf/2409.16867
+
+        ## Params:
+        - `llm: LLM`: Object of one of the final class in `iohblade.llm`.
+        - `problem: Problem`: Object instance of a class that inherits from `iohblade.problem`.
+        - `budget: int`: Max number sample that can be generated by the LLM.
+        - `iterations: int` Max number of iterations for which the algorithm is to be run.
+        - `population_size: int`: Population size (λ / µ) for the evolutionary algorithm.
+        - `use_e2_operator: bool(True)`: Use e2 operation as stated in the `moeh.prompts`; multi-individuals -> 1 individual mutation.
+        - `use_m1_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (adds novel methods.)
+        - `use_m2_operator: bool(True)`: Use m1 operation as stated in the `moeh.prompts`; 1 -> 1 mutation (used different approach.)
+        - `max_workers: int(2)`: Declaration of max `loky` workers for `joblib.Parallel`.
+
+        ## Note:
+
+        - The algorithm quits running when either of the conditons are met (||):
+            - `iterations` count of generations are iterated over.
+            - `max_sample_nums` count of samples are generated.
+        """
         super().__init__(llm, budget, name=name)
         self.iterations = iterations
         self.population_size = population_size
@@ -248,16 +261,17 @@ def __call__(self, problem: Problem):
             self.use_e2_operator,
             self.use_m1_operator,
             self.use_m2_operator,
-            1
+            1,
         )
         try:
             return self.moeh_instance.run()
         except Exception as e:
             import traceback
-            print("Some error occured, best solution till now V")
+
+            print("Some error occured:")
             print(traceback.print_exc(), e, "-----------", sep="\n")
             return self.moeh_instance.population.get_best()
-    
+
     def to_dict(self):
         """
         Returns a dictionary representation of the method including all parameters.
@@ -272,16 +286,15 @@ def to_dict(self):
             "kwargs": kwargs,
         }
 
-
-# region Database Logger:
+    # region Database Logger:
     def get_config(self) -> dict[str, Any]:
         config = {
-            "iterations" : self.iterations,
-            "max_sample_nums" : self.budget,
-            "population_size" : self.population_size,
-            "use_e2_operator" : self.use_e2_operator,
-            "use_m1_operator" : self.use_m1_operator,
-            "use_m2_operator" : self.use_m2_operator,
+            "iterations": self.iterations,
+            "max_sample_nums": self.budget,
+            "population_size": self.population_size,
+            "use_e2_operator": self.use_e2_operator,
+            "use_m1_operator": self.use_m1_operator,
+            "use_m2_operator": self.use_m2_operator,
             "minimisation": self.minimisation,
         }
 
@@ -290,9 +303,11 @@ def get_config(self) -> dict[str, Any]:
             "source": "https://github.com/XAI-liacs/BLADE/tree/main/iohblade/methods/moeh",
             "config": config,
         }
+
+
 # endregion
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     from iohblade.benchmarks.packing.circles import CirclePacking
     from iohblade.llm import Dummy_LLM
 
diff --git a/iohblade/methods/moeh_method/mutationtype.py b/iohblade/methods/moeh_method/mutationtype.py
index b3babee..e70c872 100644
--- a/iohblade/methods/moeh_method/mutationtype.py
+++ b/iohblade/methods/moeh_method/mutationtype.py
@@ -1,7 +1,7 @@
-
 from enum import Enum
 
+
 class MutationType(Enum):
-    E2 = 'e2'
-    M1 = 'm1'
-    M2 = 'm2'
\ No newline at end of file
+    E2 = "e2"
+    M1 = "m1"
+    M2 = "m2"
diff --git a/iohblade/methods/moeh_method/population.py b/iohblade/methods/moeh_method/population.py
index b296af3..89313eb 100644
--- a/iohblade/methods/moeh_method/population.py
+++ b/iohblade/methods/moeh_method/population.py
@@ -1,28 +1,33 @@
-from codebleu.syntax_match import calc_syntax_match
 import numpy as np
+from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting
+from codebleu.syntax_match import calc_syntax_match
+
+from iohblade.fitness import Fitness
 from iohblade.solution import Solution
 
+
 class Population:
     def __init__(self, size, minimisation: bool) -> None:
         self.size = size
         self._population: list[Solution] = []
+        self._history: list[Solution] = []
         self.minimisation = minimisation
+        self.best_known: list[Solution] = []
 
     def append(self, element: Solution):
         if element.fitness_is_valid():
             self._population.append(element)
-    
 
     def __len__(self):
         return len(self._population)
-    
+
     def __getitem__(self, key):
         return self._population[key]
 
     def __setitem__(self, key, value):
         self._population[key] = value
 
-    @property 
+    @property
     def population(self):
         return self._population
 
@@ -34,78 +39,83 @@ def _get_delta_domination_matrix(self) -> np.ndarray:
             for j in range(N):
                 if i != j:
                     S[i, j] = -calc_syntax_match(
-                        self._population[i].code,
-                        self._population[j].code,
-                        "python"
+                        self._population[i].code, self._population[j].code, "python"
                     )
 
-                    if self._population[i].fitness < self._population[j].fitness and not self.minimisation:
+                    if (
+                        self._population[i].fitness <= self._population[j].fitness
+                        and not self.minimisation
+                    ):
                         S[i, j] = 0
-                    elif self._population[i].fitness > self._population[j].fitness and self.minimisation:
+                    elif (
+                        self._population[i].fitness >= self._population[j].fitness
+                        and self.minimisation
+                    ):
                         S[i, j] = 0
         return S
 
-
-    def parent_selection(self, d: int, test: bool=False) -> list[Solution]:
-        print(f'Parent Selection size: {len(self._population)}.')
+    def parent_selection(self, d: int, test: bool = False) -> list[Solution]:
         S = self._get_delta_domination_matrix()
         if test:
             print(f"\tS pre column sum: \n{S}")
         if S.shape == (0, 0):
             if test:
-                print('\tEarly exit, population empty.')
+                print("\tEarly exit, population empty.")
             return []
-        
+
         v = np.sum(S, axis=0)
 
         if test:
-            print(f"\tS after column sum: {S}")
-        
+            print(f"\tS after column sum: {v}")
+
         v = v - np.max(v)
 
         if test:
-            print(f'v: {v}')
+            print(f"v: {v}")
         pdf = np.exp(v)
         pdf = pdf / pdf.sum()
         if test:
-            print(f'\tSoftmax: {pdf}')
-        idx = np.random.choice(range(len(self._population)), size=d, replace=False, p=pdf)
-        population = [self._population[i] for i in idx]
-        self._population = population
-        return population
-    
+            print(f"\tSoftmax: {pdf}")
+        idx = np.random.choice(
+            range(len(self._population)), size=d, replace=False, p=pdf
+        )
+        return [self._population[i] for i in idx]
+
     def population_management(self, test: bool = False) -> list[Solution]:
-        # if len(self._population):
-        #     return []
         M = self._get_delta_domination_matrix()
 
         if test:
-            print('Selection Matrix:')
-            print(M)
+            print("Selection Matrix:", M)
         v = np.sum(M, axis=0)
         if test:
-            print('Column wise sum:')
-            print(v)
-        k = list(map
-                    (lambda y: y[0], 
-                        sorted(enumerate(v), key=lambda x: x[1], reverse=True)
-                    )
-                )
+            print("Column wise sum:", v)
+        k = list(
+            map(lambda y: y[0], sorted(enumerate(v), key=lambda x: x[1], reverse=True))
+        )
         if test:
-            print('Sorted Index:')
-            print(k)
-        new_population : list[Solution] = [self._population[i] for i in k]
+            print("Sorted Index:", k)
+        new_population: list[Solution] = [self._population[i] for i in k[: self.size]]
+        print(len(new_population), k[: self.size])
         self._population = new_population
         return self._population
-    
+
     def get_best(self) -> list[Solution]:
-        population = list(sorted(self._population, key=lambda x: x.fitness))
-        if self.minimisation:
-            try:
-                return [population[0]]
-            except:
-                return []
-        try:
-            return [population[-1]]
-        except:
-            return []
\ No newline at end of file
+        if len(self.population) == 0:
+            return []
+
+        if isinstance(self.population[0].fitness, Fitness):
+            fitness = np.array(
+                [individual.fitness.to_vector() for individual in self._population]
+            )
+            nds = NonDominatedSorting()
+            indexes = nds.do(
+                fitness if self.minimisation else -fitness,
+                only_non_dominated_front=True,
+            )
+            return [self._population[index] for index in indexes]
+        population = list(
+            sorted(
+                self._population, key=lambda x: x.fitness, reverse=not self.minimisation
+            )
+        )
+        return [population[0]]
diff --git a/iohblade/methods/moeh_method/prompts.py b/iohblade/methods/moeh_method/prompts.py
index 255ab11..ad7e06f 100644
--- a/iohblade/methods/moeh_method/prompts.py
+++ b/iohblade/methods/moeh_method/prompts.py
@@ -76,9 +76,7 @@ def get_prompt_e2(
         return prompt_content
 
     @classmethod
-    def get_prompt_m1(
-        cls, example_prompt, format_prompt, indiv: Solution
-    ) -> str:
+    def get_prompt_m1(cls, example_prompt, format_prompt, indiv: Solution) -> str:
         prompt_content = (
             "I have one algorithm with its code as follows. \n\n\
 Algorithm's description: "
diff --git a/iohblade/solution.py b/iohblade/solution.py
index 4f855f2..bea3f79 100644
--- a/iohblade/solution.py
+++ b/iohblade/solution.py
@@ -68,7 +68,7 @@ def __setstate__(self, state):
 
     def fitness_is_valid(self) -> bool:
         """
-            Checks validity of fitness.
+        Checks validity of fitness.
         """
         fitness_values = (
             self.fitness.to_vector()
@@ -112,7 +112,7 @@ def set_scores(self, fitness: float | Fitness, feedback="", error=""):
         return self
 
     def set_scores(
-        self, fitness: float| Fitness, feedback="", error: Optional[Exception] = None
+        self, fitness: float | Fitness, feedback="", error: Optional[Exception] = None
     ):
         """
             Set the score of current instance of individual.
diff --git a/pyproject.toml b/pyproject.toml
index 39be181..ea07748 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,6 +64,7 @@ docs = [
 ]
 methods = [
     "eoh",
+    "pymoo",
     "reevo",
     "codebleu",
     "tree-sitter-python"
diff --git a/tests/unit/test_moeh.py b/tests/unit/test_moeh.py
index dc8c180..9fa2279 100644
--- a/tests/unit/test_moeh.py
+++ b/tests/unit/test_moeh.py
@@ -1,13 +1,15 @@
 import numpy as np
 import random
-from typing import Any
+from typing import Any, Optional
 
 from iohblade.llm import Dummy_LLM
+from iohblade.fitness import Fitness
 from iohblade.solution import Solution
 from iohblade.methods import MoEH_Method
 from iohblade.problem import Problem
 
 from iohblade.methods.moeh_method.prompts import MoEH_Prompts
+from iohblade.methods.moeh_method.population import Population
 
 #region Helper Classes.
 class DummyProblem(Problem):
@@ -39,12 +41,25 @@ def get_config(self) -> dict[str, Any]:
         }
     
 class DummyProblemWorks(Problem):
-    def __init__(self):
+    def __init__(self, objectives:Optional[list[str]]=None, minimisation = True):
         super().__init__()
         self.iteration = 0
+        self.objectives = objectives
+        self.minimisation = minimisation
+        self.all_scores = []
 
     def evaluate(self, solution: Solution):
+        if self.objectives:
+            score = {}
+            for obj in self.objectives:
+                score[obj] = 1 / (1 + np.e ** ((-self.iteration / 4) + 10)) * random.random()
+            score = Fitness(score)
+            self.all_scores.append(score)
+            solution.set_scores(score, f'Scored {score}.')
+            self.iteration += 1
+            return solution
         score = 1 / (1 + np.e ** ((-self.iteration / 4) + 10)) * random.random()
+        self.all_scores.append(score)
         solution.set_scores(score, f'Scored {score}, best known 1.0.')
         self.iteration += 1
         return solution
@@ -65,7 +80,7 @@ def get_config(self) -> dict[str, Any]:
             'tags': 'bruh',
             'name': 'Baka Mendo',
             'prompt': self.get_prompt(),
-            'minimisation': True,
+            'minimisation': self.minimisation,
             'evaluator':"""    def evaluate(self, solution: Solution):
         score = 1 / (np.e ** ((-self.iteration / 10) - 10))
         solution.set_scores(score, f'Scored {score}, best known 1.0.')
@@ -341,6 +356,165 @@ def __call__(self, budget = 5000) -> Solution:
 #endregion
 
 #region Population
+def test_population_matrix_returns_empty_array_when_empty():
+    p = Population(10, True)
+    x = p._get_delta_domination_matrix()
+    assert x.shape == (0, 0)
+
+def test_population_matrix_returns_appropriate_array():
+    p = Population(4, True)
+    s1 = Solution('ABC')
+    s1.set_scores(8, 'test')
+    s2 = Solution("ACD")
+    s2.set_scores(2, 'test')
+    s3 = Solution("DEF")
+    s3.set_scores(4, 'test')
+    s4 = Solution("FPG")
+    s4.set_scores(1, 'test')
+    
+    p.append(s1)
+    p.append(s2)
+    p.append(s3)
+    p.append(s4)
+
+    m = p._get_delta_domination_matrix()
+    print([x.fitness for x in p._population])
+    expected = [[0, 0, 0, 0], [-1, 0, -1, 0], [-1, 0, 0, 0], [-1, -1, -1, 0]]
+    print('\n', m)
+    for i, row in enumerate(m):
+        for j, cell in enumerate(row):
+            assert expected[i][j] == cell
+
+    p.minimisation = False
+    expected = [[0, -1, -1, -1], [0, 0, 0, -1], [0, -1, 0, -1], [0, 0, 0, 0]]
+
+    m = p._get_delta_domination_matrix()
+    print('\n', m)
+    for i, row in enumerate(m):
+        for j, cell in enumerate(row):
+            assert expected[i][j] == cell
+
+def test_parent_selection_gracefully_exits_selection_on_empty():
+    p = Population(10, True)
+    assert p.parent_selection(4) == []
+
+def test_parent_selection_works_properly():
+    p = Population(4, True)
+    s1 = Solution('ABC')
+    s1.set_scores(8, 'test')
+    s2 = Solution("ACD")
+    s2.set_scores(2, 'test')
+    s3 = Solution("DEF")
+    s3.set_scores(4, 'test')
+    s4 = Solution("FPG")
+    s4.set_scores(1, 'test')
+    
+    p.append(s1)
+    p.append(s2)
+    p.append(s3)
+    p.append(s4)
+
+    selected_population = p.parent_selection(2, True)
+    assert len(selected_population) == 2
+    for idx1, parent1 in enumerate(selected_population):
+        assert parent1 in selected_population
+        for idx2, parent2 in enumerate(selected_population):
+            if idx1 != idx2:
+                assert parent1.code != parent2.code
+
+def test_population_management_fails_gracefully():
+    p = Population(10, False)
+    p.population_management(test=True)
+
+def test_population_management_sorts_properly():
+    p = Population(10, False)
+    np.random.seed(420)
+    choices = range(ord('a'), ord('z') + 1)
+    for i in range(10):
+        code = ''
+        code = "".join([chr(np.random.choice(choices)) for _ in range(4)])
+        print(code)
+        s = Solution(code=code)
+        s.set_scores(2 ** (10 - i))
+        p.append(s)
+    
+    new_population = p.population_management(test=True)
+    for i in range(len(new_population) - 1):
+        print(new_population[i].fitness, new_population[i + 1].fitness)
+        assert new_population[i].fitness > new_population[i + 1].fitness
+
+    p.minimisation = True
+    new_population = p.population_management(True)
+    for i in range(len(new_population) - 1):
+        print(new_population[i].fitness, new_population[i + 1].fitness)
+        assert new_population[i].fitness < new_population[i + 1].fitness
+
+def test_get_best_returns_empty():
+    p = Population(10, True)
+    assert p.get_best() == []
+
+def test_get_best_handles_scalars():
+    p = Population(10, True)
+    np.random.seed(420)
+    choices = range(ord('a'), ord('z') + 1)
+    for i in range(10):
+        code = ''
+        code = "".join([chr(np.random.choice(choices)) for _ in range(4)])
+        print(code)
+        s = Solution(code=code)
+        s.set_scores(2 ** (10 - i))
+        p.append(s)
+    
+    assert len(p.get_best()) == 1
+    assert p.get_best()[0].fitness == 2
+    p.minimisation = False
+    
+    assert len(p.get_best()) == 1
+    assert p.get_best()[0].fitness == 1024
+
+def test_get_best_handles_scalars():
+    p = Population(10, True)
+    np.random.seed(420)
+    choices = range(ord('a'), ord('z') + 1)
+    for i in range(10):
+        code = "".join([chr(np.random.choice(choices)) for _ in range(4)])
+        s = Solution(code=code)
+        s.set_scores(2 ** (10 - i))
+        p.append(s)
+    
+    assert len(p.get_best()) == 1
+    assert p.get_best()[0].fitness == 2
+    p.minimisation = False
+    
+    assert len(p.get_best()) == 1
+    assert p.get_best()[0].fitness == 1024
+
+
+def test_get_best_handles_vectors():
+    p = Population(10, True)
+    problem = DummyProblemWorks(objectives=['Distance', 'Fitness'])
+    np.random.seed(69)
+    choices = range(ord('a'), ord('z') + 1)
+    for i in range(10):
+        code = "".join([chr(np.random.choice(choices)) for _ in range(4)])
+        s = Solution(code=code)
+        s = problem(s)
+        p.append(s)
+    
+    front = p.get_best()
+
+    for front_member in front:
+        for ordinary_member in p._population:
+            assert front_member.fitness <= ordinary_member.fitness
+    
+    p.minimisation = False
+
+    front = p.get_best()
+
+    for front_member in front:
+        for ordinary_member in p._population:
+            assert front_member.fitness >= ordinary_member.fitness
+
 
 #endregion
 
@@ -366,7 +540,7 @@ def test_initialise_ok():
     assert moeh.use_m2_operator == False
     assert moeh.minimisation == True
 
-def test_initialisation_fails_gracefully():
+def test_algorithm_fails_gracefully():
     problem = DummyProblem()
     llm = Dummy_LLM()
 
@@ -386,19 +560,3 @@ def test_initialisation_fails_gracefully():
     out = moeh(problem)
 
     assert len(out) == 0
-
-def test_initialiation_succeeds():
-    problem = DummyProblemWorks()
-    llm = Dummy_LLM()
-
-    moeh = MoEH_Method(
-        llm,
-        20,
-        2,
-        4,
-        True
-    )
-
-    solution = moeh(problem)
-
-    assert len(solution) == 1
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index ceba125..c658746 100644
--- a/uv.lock
+++ b/uv.lock
@@ -8,6 +8,15 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
+[[package]]
+name = "about-time"
+version = "4.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/3f/ccb16bdc53ebb81c1bf837c1ee4b5b0b69584fd2e4a802a2a79936691c0a/about-time-4.2.1.tar.gz", hash = "sha256:6a538862d33ce67d997429d14998310e1dbfda6cb7d9bbfbf799c4709847fece", size = 15380, upload-time = "2022-12-21T04:15:54.991Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fb/cd/7ee00d6aa023b1d0551da0da5fee3bc23c3eeea632fbfc5126d1fec52b7e/about_time-4.2.1-py3-none-any.whl", hash = "sha256:8bbf4c75fe13cbd3d72f49a03b02c5c7dca32169b6d49117c257e7eb3eaee341", size = 13295, upload-time = "2022-12-21T04:15:53.613Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.2"
@@ -171,6 +180,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d2/29/6533c317b74f707ea28f8d633734dbda2119bbadfc61b2f3640ba835d0f7/alembic-1.18.4-py3-none-any.whl", hash = "sha256:a5ed4adcf6d8a4cb575f3d759f071b03cd6e5c7618eb796cb52497be25bfe19a", size = 263893, upload-time = "2026-02-10T16:00:49.997Z" },
 ]
 
+[[package]]
+name = "alive-progress"
+version = "3.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "about-time" },
+    { name = "graphemeu" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9a/26/d43128764a6f8fe1668c4f87aba6b1fe52bea81d05a35c84a70d3c70b6f7/alive-progress-3.3.0.tar.gz", hash = "sha256:457dd2428b48dacd49854022a46448d236a48f1b7277874071c39395307e830c", size = 116281, upload-time = "2025-07-20T02:10:39.07Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/85/ec72f6c885703d18f3b09769645e950e14c7d0cc0a0e35d94127983f666f/alive_progress-3.3.0-py3-none-any.whl", hash = "sha256:63dd33bb94cde15ad9e5b666dbba8fedf71b72a4935d6fb9a92931e69402c9ff", size = 78403, upload-time = "2025-07-20T02:10:37.318Z" },
+]
+
 [[package]]
 name = "alphashape"
 version = "1.3.1"
@@ -355,6 +377,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" },
 ]
 
+[[package]]
+name = "autograd"
+version = "1.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/ac/4a8b58364ba865ab545251edbe475e5a35326814a5d274a2211d33ed8a5d/autograd-1.9.1.tar.gz", hash = "sha256:7818c5c69ddf9efb7da74097bd741a4c7920133f41966f68537223a15ef798bc", size = 2566975, upload-time = "2026-07-02T07:43:58.255Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6e/ce/8c98e6604bb1ec9d03c8493c328185b69bb7533fe08f3640f0f3641bd9d1/autograd-1.9.1-py3-none-any.whl", hash = "sha256:b788bae3fa010cbffb4cfb7b8ba2a3f0daa6072a8506da6164c779fe9cf3e05a", size = 54387, upload-time = "2026-07-02T07:43:56.502Z" },
+]
+
 [[package]]
 name = "autotuning-methodology"
 version = "1.2.0"
@@ -714,6 +748,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" },
 ]
 
+[[package]]
+name = "cma"
+version = "4.4.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/67/ac/8c27720838e293898671f01b5c452236a0c74f4799a3f2d5fcccbbf50d71/cma-4.4.4.tar.gz", hash = "sha256:632bd654b5dce04c0eaa3166679d3e4773ce7a79eab7934e7f363c341b9a8170", size = 316645, upload-time = "2026-02-25T22:18:16Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d4/d4/ec46cedab6a6145e21768baa8110db3e2e836a320d8499e4ef18bc894e61/cma-4.4.4-py3-none-any.whl", hash = "sha256:edb6d02eb2aac2d54650f16a8f0c70711ff17445957de7c9de92ff7fd4b7ef38", size = 328311, upload-time = "2026-02-25T22:18:09.602Z" },
+]
+
 [[package]]
 name = "codebleu"
 version = "0.7.1"
@@ -1118,6 +1164,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
+[[package]]
+name = "deprecated"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wrapt" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/49/85/12f0a49a7c4ffb70572b6c2ef13c90c88fd190debda93b23f026b25f9634/deprecated-1.3.1.tar.gz", hash = "sha256:b1b50e0ff0c1fddaa5708a2c6b0a6588bb09b892825ab2b214ac9ea9d92a5223", size = 2932523, upload-time = "2025-10-30T08:19:02.757Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/84/d0/205d54408c08b13550c733c4b85429e7ead111c7f0014309637425520a9a/deprecated-1.3.1-py2.py3-none-any.whl", hash = "sha256:597bfef186b6f60181535a29fbe44865ce137a5079f295b479886c82729d5f3f", size = 11298, upload-time = "2025-10-30T08:19:00.758Z" },
+]
+
 [[package]]
 name = "distlib"
 version = "0.4.3"
@@ -1566,6 +1624,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/81/0a861b8e1ff42960139c6cd4c7dd591292fa09ea1ae2d87677441cba4c00/gradio_client-2.5.0-py3-none-any.whl", hash = "sha256:d43e2179c29076292a76485ad7ed2e6eaa19d14ac58283bd7f5beabfe4ca958c", size = 59952, upload-time = "2026-04-20T23:16:20.186Z" },
 ]
 
+[[package]]
+name = "graphemeu"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/76/20/d012f71e7d00e0d5bb25176b9a96c5313d0e30cf947153bfdfa78089f4bb/graphemeu-0.7.2.tar.gz", hash = "sha256:42bbe373d7c146160f286cd5f76b1a8ad29172d7333ce10705c5cc282462a4f8", size = 307626, upload-time = "2025-01-15T09:48:59.488Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/69/18/36503ea63e1ecd0a95590d7b6b8b7d227a1e4541a154e1612a231def1bdc/graphemeu-0.7.2-py3-none-any.whl", hash = "sha256:1444520f6899fd30114fc2a39f297d86d10fa0f23bf7579f772f8bc7efaa2542", size = 22670, upload-time = "2025-01-15T09:48:57.241Z" },
+]
+
 [[package]]
 name = "graphene"
 version = "3.4.3"
@@ -2005,6 +2072,7 @@ kerneltuner = [
 methods = [
     { name = "codebleu" },
     { name = "eoh" },
+    { name = "pymoo" },
     { name = "reevo" },
     { name = "tree-sitter-python" },
 ]
@@ -2077,6 +2145,7 @@ kerneltuner = [
 methods = [
     { name = "codebleu", git = "https://github.com/anantashahane/codebleu?rev=main" },
     { name = "eoh", git = "https://github.com/nikivanstein/EoH?subdirectory=eoh&rev=f0d21b2f2f62c452bac545fd7f68d3af50845321" },
+    { name = "pymoo" },
     { name = "reevo", git = "https://github.com/nikivanstein/reevo.git?rev=main" },
     { name = "tree-sitter-python" },
 ]
@@ -3165,6 +3234,26 @@ wheels = [
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 ]
 
+[[package]]
+name = "moocore"
+version = "0.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi" },
+    { name = "numpy" },
+    { name = "platformdirs" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9c/95/0e36dbb8f44690a3540ca3c8c08f9000799d95a5eeec2a49abc7c76a8a0f/moocore-0.3.1.tar.gz", hash = "sha256:a8f83cfbc0aa81c1c9dd33e473adbc9b638dc3b1a6943753f8146770bb76bae4", size = 424672, upload-time = "2026-05-04T21:39:12.77Z" }
+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/40/df/c4715242db5a0d0e17578c5e643334c17d7760cd9f66c17b76a3b8267fb5/moocore-0.3.1-cp310-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:14af334b83ddf6fbdec8e5ab6e3246e85ad6b34b0f1b2c44a52e3c5561ff5530", size = 882475, upload-time = "2026-05-04T21:39:03.675Z" },
+    { url = "https://files.pythonhosted.org/packages/34/74/6230c8c2865586ce4c4b0eea994bccadd9e3e28f6acd9cade123ba8516fa/moocore-0.3.1-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bd0a65e501e704e5270d564c3b32438ddac34d22d3c5c54b01b8c897f4d949aa", size = 866948, upload-time = "2026-05-04T21:39:05.2Z" },
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+
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 version = "11.1.0"
@@ -4668,6 +4757,53 @@ wheels = [
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+[[package]]
+name = "pymoo"
+version = "0.6.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "autograd" },
+    { name = "cma" },
+    { name = "deprecated" },
+    { name = "matplotlib" },
+    { name = "moocore" },
+    { name = "numpy" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
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