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import pygad
import numpy
actual_num_fitness_calls_default_keep = 0
actual_num_fitness_calls_no_keep = 0
actual_num_fitness_calls_keep_elitism = 0
actual_num_fitness_calls_keep_parents = 0
num_generations = 50
sol_per_pop = 10
num_parents_mating = 5
function_inputs1 = [4,-2,3.5,5,-11,-4.7] # Function 1 inputs.
function_inputs2 = [-2,0.7,-9,1.4,3,5] # Function 2 inputs.
desired_output1 = 50 # Function 1 output.
desired_output2 = 30 # Function 2 output.
#### Define the fitness functions in the top-level of the module so that they are picklable and usable in the process-based parallel processing works.
#### If the functions are defined inside a class/method/function, they are not picklable and this error is raised: AttributeError: Can't pickle local object
#### Process-based parallel processing must have the used functions picklable.
def fitness_func_batch_multi(ga_instance, solution, solution_idx):
f = []
for sol in solution:
output1 = numpy.sum(sol*function_inputs1)
output2 = numpy.sum(sol*function_inputs2)
fitness1 = 1.0 / (numpy.abs(output1 - desired_output1) + 0.000001)
fitness2 = 1.0 / (numpy.abs(output2 - desired_output2) + 0.000001)
f.append([fitness1, fitness2])
return f
def fitness_func_no_batch_multi(ga_instance, solution, solution_idx):
output1 = numpy.sum(solution*function_inputs1)
output2 = numpy.sum(solution*function_inputs2)
fitness1 = 1.0 / (numpy.abs(output1 - desired_output1) + 0.000001)
fitness2 = 1.0 / (numpy.abs(output2 - desired_output2) + 0.000001)
return [fitness1, fitness2]
def fitness_func_batch_single(ga_instance, solution, solution_idx):
f = []
for sol in solution:
output = numpy.sum(solution*function_inputs1)
fitness = 1.0 / (numpy.abs(output - desired_output1) + 0.000001)
f.append(fitness)
return f
def fitness_func_no_batch_single(ga_instance, solution, solution_idx):
output = numpy.sum(solution*function_inputs1)
fitness = 1.0 / (numpy.abs(output - desired_output1) + 0.000001)
return fitness
def multi_objective_problem(keep_elitism=1,
keep_parents=-1,
fitness_batch_size=None,
stop_criteria=None,
parent_selection_type='sss',
mutation_type="random",
mutation_percent_genes="default",
multi_objective=False,
parallel_processing=None):
if fitness_batch_size is None or (type(fitness_batch_size) in pygad.GA.supported_int_types and fitness_batch_size == 1):
if multi_objective == True:
fitness_func = fitness_func_no_batch_multi
else:
fitness_func = fitness_func_no_batch_single
elif (type(fitness_batch_size) in pygad.GA.supported_int_types and fitness_batch_size > 1):
if multi_objective == True:
fitness_func = fitness_func_batch_multi
else:
fitness_func = fitness_func_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,
fitness_batch_size=fitness_batch_size,
mutation_type=mutation_type,
mutation_percent_genes=mutation_percent_genes,
keep_elitism=keep_elitism,
keep_parents=keep_parents,
stop_criteria=stop_criteria,
parent_selection_type=parent_selection_type,
parallel_processing=parallel_processing,
suppress_warnings=True)
ga_optimizer.run()
return None
def test_number_calls_fitness_function_no_parallel_processing():
multi_objective_problem(multi_objective=False,
fitness_batch_size=None,
parallel_processing=None)
def test_number_calls_fitness_function_parallel_processing_thread_1():
multi_objective_problem(multi_objective=False,
fitness_batch_size=None,
parallel_processing=['thread', 1])
def test_number_calls_fitness_function_parallel_processing_thread_2():
multi_objective_problem(multi_objective=False,
fitness_batch_size=None,
parallel_processing=['thread', 2])
def test_number_calls_fitness_function_parallel_processing_thread_5():
multi_objective_problem(multi_objective=False,
fitness_batch_size=None,
parallel_processing=['thread', 5])
def test_number_calls_fitness_function_parallel_processing_thread_5_patch_4():
multi_objective_problem(multi_objective=False,
fitness_batch_size=4,
parallel_processing=['thread', 5])
def test_number_calls_fitness_function_parallel_processing_thread_5_patch_4_multi_objective():
multi_objective_problem(multi_objective=True,
fitness_batch_size=4,
parallel_processing=['thread', 5])
def test_number_calls_fitness_function_parallel_processing_process_1():
multi_objective_problem(multi_objective=False,
fitness_batch_size=None,
parallel_processing=['process', 1])
def test_number_calls_fitness_function_parallel_processing_process_2():
multi_objective_problem(multi_objective=False,
fitness_batch_size=None,
parallel_processing=['process', 2])
def test_number_calls_fitness_function_parallel_processing_process_5():
multi_objective_problem(multi_objective=False,
fitness_batch_size=None,
parallel_processing=['process', 5])
def test_number_calls_fitness_function_parallel_processing_process_5_patch_4():
multi_objective_problem(multi_objective=False,
fitness_batch_size=4,
parallel_processing=['process', 5])
def test_number_calls_fitness_function_parallel_processing_process_5_patch_4_multi_objective():
multi_objective_problem(multi_objective=True,
fitness_batch_size=4,
parallel_processing=['process', 5])
# Stop Criteria
def test_number_calls_fitness_function_multi_objective_stop_criteria_str_single_value():
multi_objective_problem(multi_objective=True,
stop_criteria='reach_10')
def test_number_calls_fitness_function_multi_objective_stop_criteria_str():
multi_objective_problem(multi_objective=True,
stop_criteria='reach_10_20')
def test_number_calls_fitness_function_multi_objective_stop_criteria_str_decimal():
multi_objective_problem(multi_objective=True,
stop_criteria='reach_-1.0_0.5')
def test_number_calls_fitness_function_multi_objective_stop_criteria_list():
multi_objective_problem(multi_objective=True,
stop_criteria=['reach_10_20', 'reach_5_2'])
def test_number_calls_fitness_function_multi_objective_stop_criteria_list_decimal():
multi_objective_problem(multi_objective=True,
stop_criteria=['reach_-1.0_0.5', 'reach_5_-2.8'])
def test_number_calls_fitness_function_single_objective_stop_criteria_str():
multi_objective_problem(multi_objective=True,
stop_criteria='reach_10')
def test_number_calls_fitness_function_single_objective_stop_criteria_str_decimal():
multi_objective_problem(multi_objective=True,
stop_criteria='reach_-1.7')
def test_number_calls_fitness_function_single_objective_stop_criteria_list():
multi_objective_problem(multi_objective=True,
stop_criteria=['reach_10', 'reach_5'])
def test_number_calls_fitness_function_single_objective_stop_criteria_list_decimal():
multi_objective_problem(multi_objective=True,
stop_criteria=['reach_-1.5', 'reach_-2.8'])
if __name__ == "__main__":
print()
test_number_calls_fitness_function_no_parallel_processing()
print()
#### Thread-based Parallel Processing
test_number_calls_fitness_function_parallel_processing_thread_1()
print()
test_number_calls_fitness_function_parallel_processing_thread_2()
print()
test_number_calls_fitness_function_parallel_processing_thread_5()
print()
test_number_calls_fitness_function_parallel_processing_thread_5_patch_4()
print()
test_number_calls_fitness_function_parallel_processing_thread_5_patch_4_multi_objective()
print()
#### Thread-based Parallel Processing
test_number_calls_fitness_function_parallel_processing_process_1()
print()
test_number_calls_fitness_function_parallel_processing_process_2()
print()
test_number_calls_fitness_function_parallel_processing_process_5()
print()
test_number_calls_fitness_function_parallel_processing_process_5_patch_4()
print()
test_number_calls_fitness_function_parallel_processing_process_5_patch_4_multi_objective()
print()
#### Multi-Objective Stop Criteria
test_number_calls_fitness_function_multi_objective_stop_criteria_str_single_value()
print()
test_number_calls_fitness_function_multi_objective_stop_criteria_str()
print()
test_number_calls_fitness_function_multi_objective_stop_criteria_str_decimal()
print()
test_number_calls_fitness_function_multi_objective_stop_criteria_list()
print()
test_number_calls_fitness_function_multi_objective_stop_criteria_list_decimal()
print()
#### Single-Objective Stop Criteria
test_number_calls_fitness_function_single_objective_stop_criteria_str()
print()
test_number_calls_fitness_function_single_objective_stop_criteria_str_decimal()
print()
test_number_calls_fitness_function_single_objective_stop_criteria_list()
print()
test_number_calls_fitness_function_single_objective_stop_criteria_list_decimal()
print()