diff --git a/modnet/hyper_opt/fit_genetic.py b/modnet/hyper_opt/fit_genetic.py
index 7d0b8b7f..9e6ea7f7 100644
--- a/modnet/hyper_opt/fit_genetic.py
+++ b/modnet/hyper_opt/fit_genetic.py
@@ -465,13 +465,21 @@ def function_fitness(
         else:
             n_splits = num_nested_folds
         train_val_datas = []
+        sample_weights = []
         for train, val in splits:
             train_val_datas.append(self.train_data.split((train, val)))
+            if "sample_weight" in pop[0].fit_params:
+                sample_weights.append(pop[0].fit_params["sample_weight"][train])
+            else:
+                sample_weights.append(None)
 
         tasks = []
         for i, individual in enumerate(pop):
             for j in range(n_splits):
                 train_data, val_data = train_val_datas[j]
+                sample_weight = sample_weights[j]
+                if sample_weight is not None:
+                    individual.fit_params["sample_weight"] = sample_weight
                 tasks += [
                     {
                         "individual": individual,
@@ -653,6 +661,10 @@ def run(
 
             self.best_model = EnsembleMODNetModel(models=ensemble)
             """
+            if "sample_weight" in fit_params:
+                self.best_individual.fit_params["sample_weight"] = fit_params[
+                    "sample_weight"
+                ]
             self.best_model = self.best_individual.refit_model(
                 self.data, n_models=refit, n_jobs=n_jobs or 1, fast=fast
             )