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/__init__.py b/iohblade/methods/__init__.py
index d4c965b..6088031 100644
--- a/iohblade/methods/__init__.py
+++ b/iohblade/methods/__init__.py
@@ -3,6 +3,8 @@
 from .random_search import RandomSearch
 from .lhns import LHNS_Method
 from .mcts_ahd import MCTS_Method
+from .moeh_method.mutationtype import MutationType
+from .moeh_method.moeh import MoEH_Method
 
 try:
     from .eoh import EoH
@@ -20,4 +22,6 @@
     "ReEvo",
     "LHNS_Method",
     "MCTS_Method",
+    "MoEH_Method",
+    "MutationType",
 ]
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_method/__init__.py b/iohblade/methods/moeh_method/__init__.py
new file mode 100644
index 0000000..02f3069
--- /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"]
diff --git a/iohblade/methods/moeh_method/moeh.py b/iohblade/methods/moeh_method/moeh.py
new file mode 100644
index 0000000..01aec5d
--- /dev/null
+++ b/iohblade/methods/moeh_method/moeh.py
@@ -0,0 +1,320 @@
+import random
+import inspect
+from typing import Any
+
+from iohblade.llm import LLM
+from iohblade.method import Method
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+
+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:
+    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.
+        """
+
+        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._remianing_budget = max_sample_nums
+        self.population_size = population_size
+        self.iterations = iterations
+
+        self.minimisation = minimisation
+        config = self.problem.get_config()
+        assert (
+            self.minimisation == config["minimisation"]
+        ), "Problem.minimisation and minimisation parameter passed must match."
+
+        # Runtime parameters:
+        self._current_iteration = 0
+        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):
+        """
+        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.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)
+        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))
+        solution = self.llm.sample_solution(message, parent_ids)
+        return 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`.
+        """
+        match (type):
+            case MutationType.M1:
+                prompt = MoEH_Prompts.get_prompt_m1(
+                    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]
+                )
+                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
+                )
+                solution = self._sample_solution(prompt, pop)
+                return solution
+
+    def evolve_solution(self, population_index: int, pop: list[Solution]) -> Solution:
+        mutation_type = random.choice(self.allowed_mutation_types)
+        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.get_fitness(solution)
+        self.max_sample_nums -= 1
+        return solution
+
+    def run(self) -> list[Solution]:
+        new_population: list[Solution] = []
+
+        self.initialise()
+        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 = []
+            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()
+            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.
+        """
+        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
+        self.minimisation = minimisation
+        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):
+        self.moeh_instance = MoEH(
+            self.llm,
+            problem,
+            self.iterations,
+            self.budget,
+            self.population_size,
+            self.minimisation,
+            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:")
+            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 "MoEH",
+            "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.minimisation,
+        }
+
+        return {
+            "name": self.name,
+            "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)
+
+    for key, value in moeh.get_config().items():
+        print(f"==========================={key}===========================")
+        print(value)
diff --git a/iohblade/methods/moeh_method/mutationtype.py b/iohblade/methods/moeh_method/mutationtype.py
new file mode 100644
index 0000000..e70c872
--- /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"
diff --git a/iohblade/methods/moeh_method/population.py b/iohblade/methods/moeh_method/population.py
new file mode 100644
index 0000000..89313eb
--- /dev/null
+++ b/iohblade/methods/moeh_method/population.py
@@ -0,0 +1,121 @@
+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
+    def population(self):
+        return self._population
+
+    def _get_delta_domination_matrix(self) -> np.ndarray:
+        N = len(self._population)
+        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
+        return S
+
+    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.")
+            return []
+
+        v = np.sum(S, axis=0)
+
+        if test:
+            print(f"\tS after column sum: {v}")
+
+        v = v - np.max(v)
+
+        if test:
+            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
+        )
+        return [self._population[i] for i in idx]
+
+    def population_management(self, test: bool = False) -> list[Solution]:
+        M = self._get_delta_domination_matrix()
+
+        if test:
+            print("Selection Matrix:", M)
+        v = np.sum(M, axis=0)
+        if test:
+            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:", 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]:
+        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
new file mode 100644
index 0000000..ad7e06f
--- /dev/null
+++ b/iohblade/methods/moeh_method/prompts.py
@@ -0,0 +1,112 @@
+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) -> str:
+        return "\n".join([task_prompt, example_prompt, format_prompt])
+
+    @classmethod
+    def get_prompt_e2(
+        cls, example_prompt, format_prompt, indivs: list[Solution]
+    ) -> str:
+        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. \
+"
+            + example_prompt
+            + format_prompt
+        )
+        return prompt_content
+
+    @classmethod
+    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: "
+            + 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([example_prompt, format_prompt])
+        return prompt_content
+
+    @classmethod
+    def get_prompt_m2(
+        cls, 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: "
+            + 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([example_prompt, format_prompt])
+        return prompt_content
diff --git a/iohblade/solution.py b/iohblade/solution.py
index dfaf42a..bea3f79 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
@@ -66,6 +66,17 @@ def __setstate__(self, state):
         if self.configspace == "":
             self.configspace = None
 
+    def fitness_is_valid(self) -> bool:
+        """
+        Checks validity of fitness.
+        """
+        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):
         """
         Sets the operator name that generated this individual.
@@ -94,14 +105,14 @@ 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
         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 +131,21 @@ 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 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"\tOn line {line_no}: {code_line}.\n"
+                        )
+            except:
+                self.error = repr(error)
 
         return self
 
diff --git a/pyproject.toml b/pyproject.toml
index 5f79fa6..ea07748 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]
@@ -64,7 +64,10 @@ docs = [
 ]
 methods = [
     "eoh",
+    "pymoo",
     "reevo",
+    "codebleu",
+    "tree-sitter-python"
 ]
 extraproblems = [
     "pymoosh==3.2",
@@ -93,6 +96,7 @@ 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_moeh.py b/tests/unit/test_moeh.py
new file mode 100644
index 0000000..9fa2279
--- /dev/null
+++ b/tests/unit/test_moeh.py
@@ -0,0 +1,562 @@
+import numpy as np
+import random
+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):
+    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.get_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, 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
+    
+    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.get_prompt(),
+            '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.')
+        self.iteration += 1
+        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
+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
+
+def test_initialise_ok():
+    llm = Dummy_LLM()
+    moeh = MoEH_Method(
+        llm=llm,
+        budget=10,
+        population_size=2,
+        iterations=2,
+        use_e2_operator=True,
+        use_m1_operator=False,
+        use_m2_operator=False,
+        minimisation=True
+    )
+
+    assert moeh.llm == llm
+    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_algorithm_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,
+        minimisation=True
+    )
+
+
+
+    out = moeh(problem)
+
+    assert len(out) == 0
diff --git a/tests/unit/test_population.py b/tests/unit/test_population.py
new file mode 100644
index 0000000..0cfb8a2
--- /dev/null
+++ b/tests/unit/test_population.py
@@ -0,0 +1,58 @@
+from iohblade.solution import Solution
+from iohblade.methods.moeh_method.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, True)
+    p.append(s)
+
+    assert len(p) == 1
+    
+
+def test_population_rejects_invalid_solution():
+    s = Solution()
+
+    p = Population(10, True)
+
+    p.append(s)
+
+    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/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.'
diff --git a/uv.lock b/uv.lock
index bc83eaf..a68c6ff 100644
--- a/uv.lock
+++ b/uv.lock
@@ -8,13 +8,22 @@ resolution-markers = [
     "python_full_version < '3.12'",
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+[[package]]
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+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]]
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+version = "2.7.1"
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-sdist = { url = "https://files.pythonhosted.org/packages/33/c6/61a2d7b7572279226bb2e7f61d7a19ca7c90da0329c93fa0d560cbf288d8/aiohappyeyeballs-2.6.2.tar.gz", hash = "sha256:e202810ee718bd01fc6ef49e8ea53d023d5cb6b581076d7925aa499fa55dbe64", size = 22591, upload-time = "2026-05-20T15:12:24.631Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ce/f4/eec0465c2f67b2664688d0240b3212d5196fd89e741df67ddb81f8d35658/aiohappyeyeballs-2.7.1.tar.gz", hash = "sha256:065665c041c42a5938ed220bdcd7230f22527fbec085e1853d2402c8a3615d9d", size = 24757, upload-time = "2026-07-01T17:11:55.501Z" }
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+    { url = "https://files.pythonhosted.org/packages/71/43/1947f06babed6b3f1d7f38b0c767f52df66bfb2bc10b468c4a7de9eceff2/aiohappyeyeballs-2.7.1-py3-none-any.whl", hash = "sha256:9243213661e29250eb41368e5daa826fc017156c3b8a11440826b2e3ed376472", size = 15038, upload-time = "2026-07-01T17:11:54.055Z" },
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 [[package]]
@@ -159,16 +168,29 @@ wheels = [
 
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-version = "1.18.4"
+version = "1.18.5"
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     { name = "sqlalchemy" },
     { name = "typing-extensions" },
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+sdist = { url = "https://files.pythonhosted.org/packages/1a/cc/ac0bed8e562e7407fe55c3ba85a4dce86e6dbd8730887bd1e406a6c5c18a/alembic-1.18.5.tar.gz", hash = "sha256:1554982221dd17e9a749b53902407578eb305e453f71999e8c7f0a48389fff8e", size = 2060480, upload-time = "2026-06-25T15:20:54.888Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/96/78/5fe6dc3a3a5b2f5a2a4faef8bfe336d5fa049a38884ab3172e0098160c01/alembic-1.18.5-py3-none-any.whl", hash = "sha256:06d8ba9d04558022f5395e9317de03d270f3dced49cee01f89fe7a13c26f14bc", size = 264664, upload-time = "2026-06-25T15:20:56.673Z" },
+]
+
+[[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" }
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+    { 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" },
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 [[package]]
@@ -181,7 +203,8 @@ 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" },
 ]
@@ -192,7 +215,7 @@ wheels = [
 
 [[package]]
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-version = "6.2.1"
+version = "6.2.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jinja2" },
@@ -201,9 +224,9 @@ dependencies = [
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+sdist = { url = "https://files.pythonhosted.org/packages/06/a1/5e6cc638a66da48cfc89a79c2f4810dfec00b63385f9b009ab1f069779bb/altair-6.2.2.tar.gz", hash = "sha256:a1ff9d9cfe81c75414641826312b9471780e19d39293ba0b012933f6b6cba0fe", size = 766606, upload-time = "2026-06-23T12:47:13.384Z" }
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+    { url = "https://files.pythonhosted.org/packages/e3/99/d6031f4f146298951c46b1bf1cc160c2a63f6e44b3c13a30054add100d5f/altair-6.2.2-py3-none-any.whl", hash = "sha256:94014f8ad8617c3cb163d1137359cd6db5ba134b9b46d93cfd8b609fd245a583", size = 797613, upload-time = "2026-06-23T12:47:11.451Z" },
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 [[package]]
@@ -226,7 +249,7 @@ wheels = [
 
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-version = "0.109.2"
+version = "0.116.0"
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 dependencies = [
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@@ -238,9 +261,9 @@ dependencies = [
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     { name = "typing-extensions" },
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-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/66/a2/d31f14e28d49bae983a3634e38dfb4b31c50110b5e403596c5c6a20b23f8/anthropic-0.116.0.tar.gz", hash = "sha256:5fc248fbb9fe03ef686f8a774f81586bca31a043260aab88b387ea3660f4a396", size = 949149, upload-time = "2026-07-02T19:08:10.534Z" }
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+    { url = "https://files.pythonhosted.org/packages/c7/dd/2a1e81cf1b163acc340afc4ec74ed1d86f5eed1a809fabdeed3e0997b346/anthropic-0.116.0-py3-none-any.whl", hash = "sha256:6c0a7698e8d652455da3499978279bb2588c7264d0a35be3666009a4258c8256", size = 956896, upload-time = "2026-07-02T19:08:08.756Z" },
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 [[package]]
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 [[package]]
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+sdist = { url = "https://files.pythonhosted.org/packages/3b/72/5562aabb8dd7181e8e860622a38bea08d17842b99ecd4c91f84ac95251b0/anyio-4.14.1.tar.gz", hash = "sha256:8d648a3544c1a700e3ff78615cd679e4c5c3f149904287e73687b2596963629e", size = 254831, upload-time = "2026-06-24T20:56:06.017Z" }
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+[[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"
@@ -366,7 +401,8 @@ 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 +493,7 @@ wheels = [
 
 [[package]]
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-version = "4.5.0"
+version = "4.7.0"
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 dependencies = [
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@@ -467,39 +503,85 @@ dependencies = [
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-    { name = "textual" },
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