track_final_chromosome = []
# Must rename this to match what you are testing
for def_num_populations in param_change:
# Start timer at current time
start_time = time.time()
# Create the MP GA object
mp_ga = MPGeneticAlgorithm(
input_data=seed,
fitness_function=sphere,
num_genes=50,
num_populations=def_num_populations,
population_size=def_population_size,
generations=def_generations,
crossover_probability=def_crossover_probability,
mutation_probability=def_mutation_probability,
migration_probability=def_migration_probability,
migration_frequency=def_migration_frequency,
elitism_ratio=def_elitism_ratio,
)
# Run the MP GA
mp_ga.run()
# Get the best Chromosome from the MP GA run
best_chromosomes = mp_ga.get_best_chromosomes()
# Print the execution time of the MP GA
run_time = time.time() - start_time
print("Run Time: %s seconds" % (run_time))
param_change = [10, 20, 30, 40, 50, 60, 70, 80, 90]
track_run_time = []
track_final_fit_value = []
track_max_fitness = []
track_final_chromosome = []
for param in param_change:
# Create the MP GA object
mp_ga = MPGeneticAlgorithm(
input_data=[param] * num_populations,
fitness_function=fitness_function,
num_genes=num_genes,
num_populations=num_populations,
population_size=population_size,
generations=generations,
crossover_probability=crossover_probability,
mutation_probability=mutation_probability,
migration_probability=migration_probability,
migration_frequency=migration_frequency,
elitism_ratio=elitism_ratio,
)
# Run the MP GA
mp_ga.run()
# Get the best Chromosome from the MP GA run
best_chromosomes = mp_ga.get_best_chromosomes()
# Print the execution time of the MP GA
run_time = time.time() - start_time
print("Run Time: %s seconds\n" % run_time)
# Values refer to the PERCENTAGE of a one in a Chromosome for each population
# IMPORTANT: Each value must be between 0 and 100
seed = [10, 20, 30, 40, 50]
# Start timer at current time
start_time = time.time()
# Create the MP GA object
mp_ga = MPGeneticAlgorithm(
input_data=seed,
fitness_function=sphere,
num_genes=50,
num_populations=5,
population_size=50,
generations=100,
crossover_probability=0.8,
mutation_probability=0.01,
migration_probability=0.1,
migration_frequency=1,
elitism_ratio=0.02,
)
# Run the MP GA
mp_ga.run()
# Get the best Chromosome from the MP GA run
best_chromosomes = mp_ga.get_best_chromosomes()
# Print the execution time of the MP GA
print("Run Time: %s seconds" % (time.time() - start_time))