Search now returns multiple individuals, for multi-objective support

examples/example.py

@@ -56,17 +56,17 @@ def target(x):
 
 
 alg_gp = GeneticProgramming(problem=problem, budget=budget, representation=representation)
-ind = alg_gp.search()
+ind = alg_gp.search()[0]
 print("\n======\nGP\n======\n")
 print(f"{ind.get_fitness(problem)} - {ind}")
 
 
 alg_hc = HC(problem=problem, budget=budget, representation=representation)
-ind = alg_hc.search()
+ind = alg_hc.search()[0]
 print("\n======\nHC\n======\n")
 print(f"{ind.get_fitness(problem)} - {ind}")
 
 alg_rs = RandomSearch(problem=problem, budget=budget, representation=representation)
-ind = alg_rs.search()
+ind = alg_rs.search()[0]
 print("\n======\nRS\n======\n")
 print(f"{ind.get_fitness(problem)} - {ind}")

examples/extra_columns_example.py

@@ -59,5 +59,5 @@ def fitness_test(p) -> float:
     seed=SEED,
 )
 
-ind = gp.search()
+ind = gp.search()[0]
 print(simplify(ind.get_phenotype()))

tests/core/reproducibility_test.py

@@ -162,7 +162,8 @@ def test_random_gp(representation):
             representation=repr,
             random=r,
         )
-        e = gp.search()
+        es = gp.search()
+        e = es[0]
         v = e.get_phenotype().i, e.get_phenotype().j
         vals.append(v)
     assert vals[0] == vals[1]

tests/geml/symbolic_regression_test.py

@@ -75,5 +75,5 @@ def fitness_function(x: Expression) -> float:
         random=r,
         budget=EvaluationBudget(100),
     )
-    ind = gp.search()
+    ind = gp.search()[0]
     assert ind.get_fitness(p).fitness_components[0] <= 1

tests/gp/parallel_test.py

@@ -67,7 +67,7 @@ def test_parallel(self):
             population_initializer=FullInitializer(max_depth=max_depth),
             tracker=tracker,
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, UnderTest)
         assert isinstance(tree.a, Leaf)

tests/gp/probabilistic_test.py

@@ -47,7 +47,7 @@ def test_probabilistic_grammar_tree_based(self):
             population_size=1000,
             budget=EvaluationBudget(50 * 1000),
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, OptionA)
 
@@ -65,7 +65,7 @@ def test_probabilistic_grammar_ge(self):
             population_size=1000,
             budget=EvaluationBudget(50 * 1000),
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, OptionA)
 
@@ -83,7 +83,7 @@ def test_probabilistic_grammar_sge(self):
             population_size=1000,
             budget=EvaluationBudget(50 * 1000),
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, OptionA)
 
@@ -102,6 +102,6 @@ def test_probabilistic_grammar_dsge(self):
             random=r,
             population_size=1000,
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, OptionA)

tests/gp/type_safety_test.py

@@ -64,7 +64,7 @@ def test_safety(self):
                 GenericMutationStep(1),
             ),
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, UnderTest)
         assert isinstance(tree.a, Leaf)

tests/gp/unknown_objectives_test.py

@@ -51,7 +51,7 @@ def test_unknown_objectives_and_target(self):
                 GenericMutationStep(1),
             ),
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, Root)
         assert isinstance(tree, Leaf)
@@ -75,7 +75,7 @@ def test_unknown_objectives_and_unkown_target(self):
                 GenericMutationStep(1),
             ),
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         tree = ind.get_phenotype()
         assert isinstance(tree, Root)
         assert isinstance(tree, Leaf)

tests/representations/basic_representation_test.py

@@ -1,4 +1,5 @@
 import copy
+from typing import Any
 from geneticengine.algorithms.hill_climbing import HC
 from geneticengine.algorithms.one_plus_one import OnePlusOne
 from geneticengine.algorithms.random_search import RandomSearch
@@ -18,8 +19,8 @@ def create_genotype(self, random: RandomSource, **kwargs) -> list[int]:
     def genotype_to_phenotype(self, internal: list[int]) -> int:
         return sum(internal)
 
-    def mutate(self, random: RandomSource, internal: list[int]) -> list[int]:
-        nc = copy.deepcopy(internal)
+    def mutate(self, random: RandomSource, genotype: list[int], **kwargs: Any) -> list[int]:
+        nc = copy.deepcopy(genotype)
         ind = random.randint(0, len(nc) - 1)
         nc[ind] = random.randint(0, MAX_NUMBER)
         return nc
@@ -29,20 +30,20 @@ class TestBasicRepresentation:
     def test_random_search(self):
         p = SingleObjectiveProblem(fitness_function=lambda x: abs(2024 - x), minimize=True)
         rs = RandomSearch(problem=p, budget=EvaluationBudget(100), representation=LinearRepresentation())
-        v = rs.search()
+        v = rs.search()[0]
         assert isinstance(v.get_phenotype(), int)
         assert 0 <= v.get_phenotype() <= MAX_NUMBER * MAX_ELEMENTS
 
     def test_one_plus_one(self):
         p = SingleObjectiveProblem(fitness_function=lambda x: abs(2024 - x), minimize=True)
         rs = OnePlusOne(problem=p, budget=EvaluationBudget(100), representation=LinearRepresentation())
-        v = rs.search()
+        v = rs.search()[0]
         assert isinstance(v.get_phenotype(), int)
         assert 0 <= v.get_phenotype() <= MAX_NUMBER * MAX_ELEMENTS
 
     def test_hill_climbing(self):
         p = SingleObjectiveProblem(fitness_function=lambda x: abs(2024 - x), minimize=True)
         rs = HC(problem=p, budget=EvaluationBudget(100), representation=LinearRepresentation())
-        v = rs.search()
+        v = rs.search()[0]
         assert isinstance(v.get_phenotype(), int)
         assert 0 <= v.get_phenotype() <= MAX_NUMBER * MAX_ELEMENTS

tests/representations/dependent_types_test.py

@@ -37,7 +37,7 @@ def fitness_function(x: SimplePair) -> float:
             random=r,
             budget=EvaluationBudget(10),
         )
-        ind = gp.search()
+        ind = gp.search()[0]
         p = ind.get_phenotype()
         assert p.a <= p.b