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More tests for NSGA-II
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@ahmedfgad
ahmedfgad committed Nov 6, 2023
1 parent 210c345 commit 2865a2c
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Showing 2 changed files with 94 additions and 17 deletions.
2 changes: 1 addition & 1 deletion pygad/pygad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -880,7 +880,7 @@ def __init__(self,
self.select_parents = self.rank_selection
else:
self.valid_parameters = False
raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n")
raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (steady state selection)\n-rws (roulette wheel selection)\n-sus (stochastic universal selection)\n-rank (rank selection)\n-random (random selection)\n-tournament (tournament selection)\n-tournament_nsga2: (Tournament selection for NSGA-II)\n-nsga2: (NSGA-II parent selection).\n")

# For tournament selection, validate the K value.
if (parent_selection_type == "tournament"):
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109 changes: 93 additions & 16 deletions tests/test_number_fitness_function_calls.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,20 +14,33 @@
def number_calls_fitness_function(keep_elitism=1,
keep_parents=-1,
mutation_type="random",
mutation_percent_genes="default"):
mutation_percent_genes="default",
parent_selection_type='sss',
multi_objective=False):

actual_num_fitness_calls = 0
def fitness_func(ga, solution, idx):
def fitness_func_no_batch_single(ga, solution, idx):
nonlocal actual_num_fitness_calls
actual_num_fitness_calls = actual_num_fitness_calls + 1
return 1

def fitness_func_no_batch_multi(ga_instance, solution, solution_idx):
nonlocal actual_num_fitness_calls
actual_num_fitness_calls = actual_num_fitness_calls + 1
return [1, 1]

if multi_objective == True:
fitness_func = fitness_func_no_batch_multi
else:
fitness_func = fitness_func_no_batch_single

ga_optimizer = pygad.GA(num_generations=num_generations,
sol_per_pop=sol_per_pop,
num_genes=6,
num_parents_mating=num_parents_mating,
fitness_func=fitness_func,
mutation_type=mutation_type,
parent_selection_type=parent_selection_type,
mutation_percent_genes=mutation_percent_genes,
keep_elitism=keep_elitism,
keep_parents=keep_parents,
Expand Down Expand Up @@ -64,46 +77,68 @@ def test_number_calls_fitness_function_default_keep():
actual, expected = number_calls_fitness_function()
assert actual == expected

def test_number_calls_fitness_function_no_keep():
def test_number_calls_fitness_function_no_keep(multi_objective=False,
parent_selection_type='sss'):
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0)
keep_parents=0,
parent_selection_type=parent_selection_type,
multi_objective=multi_objective)
assert actual == expected

def test_number_calls_fitness_function_keep_elitism():
def test_number_calls_fitness_function_keep_elitism(multi_objective=False,
parent_selection_type='sss'):
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=0)
keep_parents=0,
parent_selection_type=parent_selection_type,
multi_objective=multi_objective)
assert actual == expected

def test_number_calls_fitness_function_keep_parents():
def test_number_calls_fitness_function_keep_parents(multi_objective=False,
parent_selection_type='sss'):
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=4)
keep_parents=4,
parent_selection_type=parent_selection_type,
multi_objective=multi_objective)
assert actual == expected

def test_number_calls_fitness_function_both_keep():
def test_number_calls_fitness_function_both_keep(multi_objective=False,
parent_selection_type='sss'):
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4)
keep_parents=4,
parent_selection_type=parent_selection_type,
multi_objective=multi_objective)
assert actual == expected

def test_number_calls_fitness_function_no_keep_adaptive_mutation():
def test_number_calls_fitness_function_no_keep_adaptive_mutation(multi_objective=False,
parent_selection_type='sss'):
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
parent_selection_type=parent_selection_type,
mutation_type="adaptive",
mutation_percent_genes=[10, 5])
mutation_percent_genes=[10, 5],
multi_objective=multi_objective)
assert actual == expected

def test_number_calls_fitness_function_default_adaptive_mutation():
def test_number_calls_fitness_function_default_adaptive_mutation(multi_objective=False,
parent_selection_type='sss'):
actual, expected = number_calls_fitness_function(mutation_type="adaptive",
mutation_percent_genes=[10, 5])
parent_selection_type=parent_selection_type,
mutation_percent_genes=[10, 5],
multi_objective=multi_objective)
assert actual == expected

def test_number_calls_fitness_function_both_keep_adaptive_mutation():
def test_number_calls_fitness_function_both_keep_adaptive_mutation(multi_objective=False,
parent_selection_type='sss'):
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
parent_selection_type=parent_selection_type,
mutation_type="adaptive",
mutation_percent_genes=[10, 5])
mutation_percent_genes=[10, 5],
multi_objective=multi_objective)
assert actual == expected

if __name__ == "__main__":
#### Single-objective
print()
test_number_calls_fitness_function_default_keep()
print()
Expand All @@ -121,3 +156,45 @@ def test_number_calls_fitness_function_both_keep_adaptive_mutation():
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation()
print()

#### Multi-Objective
print()
test_number_calls_fitness_function_no_keep(multi_objective=True)
print()
test_number_calls_fitness_function_keep_elitism(multi_objective=True)
print()
test_number_calls_fitness_function_keep_parents(multi_objective=True)
print()
test_number_calls_fitness_function_both_keep(multi_objective=True)
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation(multi_objective=True)
print()
test_number_calls_fitness_function_default_adaptive_mutation(multi_objective=True)
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation(multi_objective=True)
print()

#### Multi-Objective NSGA-II Parent Selection
print()
test_number_calls_fitness_function_no_keep(multi_objective=True,
parent_selection_type='nsga2')
print()
test_number_calls_fitness_function_keep_elitism(multi_objective=True,
parent_selection_type='nsga2')
print()
test_number_calls_fitness_function_keep_parents(multi_objective=True,
parent_selection_type='nsga2')
print()
test_number_calls_fitness_function_both_keep(multi_objective=True,
parent_selection_type='nsga2')
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation(multi_objective=True,
parent_selection_type='nsga2')
print()
test_number_calls_fitness_function_default_adaptive_mutation(multi_objective=True,
parent_selection_type='nsga2')
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation(multi_objective=True,
parent_selection_type='nsga2')
print()

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