diff --git a/cgp/individual.py b/cgp/individual.py
index b6db334c..428c9d27 100644
--- a/cgp/individual.py
+++ b/cgp/individual.py
@@ -32,6 +32,7 @@ def __init__(self, fitness: Union[float, None]) -> None:
         """
         self.fitness: Union[float, None] = fitness
         self.idx: int
+        self.parent_idx: int
 
     def clone(self):
         raise NotImplementedError()
@@ -105,7 +106,9 @@ def __repr__(self):
         return f"Individual(idx={self.idx}, fitness={self.fitness}, genome={self.genome}))"
 
     def clone(self) -> "IndividualSingleGenome":
-        return IndividualSingleGenome(self.fitness, self.genome.clone())
+        ind = IndividualSingleGenome(self.fitness, self.genome.clone())
+        ind.parent_idx = self.idx
+        return ind
 
     def mutate(self, mutation_rate: float, rng: np.random.RandomState) -> None:
         only_silent_mutations = self._mutate_genome(self.genome, mutation_rate, rng)
@@ -149,7 +152,9 @@ def __init__(self, fitness: Union[float, None], genome: List[Genome]) -> None:
         self.genome: List[Genome] = genome
 
     def clone(self) -> "IndividualMultiGenome":
-        return IndividualMultiGenome(self.fitness, [g.clone() for g in self.genome])
+        ind = IndividualMultiGenome(self.fitness, [g.clone() for g in self.genome])
+        ind.parent_idx = self.idx
+        return ind
 
     def mutate(self, mutation_rate: float, rng: np.random.RandomState) -> None:
         for g in self.genome:
diff --git a/test/test_ea_mu_plus_lambda.py b/test/test_ea_mu_plus_lambda.py
index 8a258a4d..5ed9f750 100644
--- a/test/test_ea_mu_plus_lambda.py
+++ b/test/test_ea_mu_plus_lambda.py
@@ -59,6 +59,25 @@ def objective(ind):
         assert ind.idx == len(pop.parents) + idx
 
 
+def test_offspring_individuals_are_assigned_correct_parent_indices(
+    population_params, genome_params
+):
+    def objective(ind):
+        ind.fitness = 0.0
+        return ind
+
+    population_params["n_parents"] = 1
+    pop = cgp.Population(**population_params, genome_params=genome_params)
+
+    ea = cgp.ea.MuPlusLambda(10, 10, 1)
+    ea.initialize_fitness_parents(pop, objective)
+
+    offsprings = ea._create_new_offspring_generation(pop)
+
+    for ind in offsprings:
+        assert ind.parent_idx == 0
+
+
 def test_local_search_is_only_applied_to_best_k_individuals(
     population_params, local_search_params
 ):