Simplify sorting of individuals in mu_plus_lambda

cgp/ea/mu_plus_lambda.py

@@ -1,7 +1,7 @@
 import concurrent.futures
 import numpy as np
 
-from typing import Callable, List, Tuple
+from typing import Callable, List
 
 from ..individual import IndividualBase
 from ..population import Population
@@ -151,33 +151,22 @@ def _compute_fitness(
         return combined
 
     def _sort(self, combined: List[IndividualBase]) -> List[IndividualBase]:
-        # create copy of population
-        combined_copy = [ind.clone() for ind in combined]
+        def sort_func(ind: IndividualBase) -> float:
+            """Return fitness of an individual, return -infinity for individuals
+            with fitness equal nan, or raise error if it is None.
 
-        # replace all nan by -inf to make sure they end up at the end
-        # after sorting
-        for ind in combined_copy:
+            """
             if np.isnan(ind.fitness):
-                ind.fitness = -np.inf
+                return -np.inf
 
-        def sort_func(zipped_ind: Tuple[int, IndividualBase]) -> float:
-            """Return fitness of an individual or raise error if it is None.
-            """
-            _, ind = zipped_ind
             if isinstance(ind.fitness, float):
                 return ind.fitness
             else:
                 raise ValueError(
                     f"IndividualBase fitness value is of wrong type {type(ind.fitness)}."
                 )
 
-        # get list of indices that sorts combined_copy ("argsort") in descending order
-        combined_sorted_indices = [
-            idx for (idx, _) in sorted(enumerate(combined_copy), key=sort_func, reverse=True)
-        ]
-
-        # return original list of individuals sorted in descending order
-        return [combined[idx] for idx in combined_sorted_indices]
+        return sorted(combined, key=sort_func, reverse=True)
 
     def _create_new_parent_population(
         self, n_parents: int, combined: List[IndividualBase]

test/test_ea_mu_plus_lambda.py

@@ -28,9 +28,9 @@ def objective_with_label(individual, label):
     assert pop.champion.fitness == pytest.approx(-1.0)
 
 
-def test_fitness_contains_nan(population_params, genome_params):
+def test_fitness_contains_and_maintains_nan(population_params, genome_params):
     def objective(individual):
-        if np.random.rand() < 0.5:
+        if np.random.rand() < 0.95:
             individual.fitness = np.nan
         else:
             individual.fitness = np.random.rand()
@@ -41,3 +41,5 @@ def objective(individual):
     ea = cgp.ea.MuPlusLambda(10, 10, 1)
     ea.initialize_fitness_parents(pop, objective)
     ea.step(pop, objective)
+
+    assert np.nan in [ind.fitness for ind in pop]