def Run_GA(options, cities):
# Log
logging.debug('Running Genetic Algorithm')
# Build the Initial Population
population = Tour_Manager(cities).Build_Population(
options.ga_params['population_size'])
# Construct Genetic Algorithm
ga = Genetic_Algorithm(
population=population,
fitness_function=lambda Tour: Tour.distance,
crossover_algorithm=Tour.Tour.Crossover,
selection_rate=options.ga_params['selection_rate'],
mutation_algorithm=Tour.Tour.Mutate,
mutation_rate=options.ga_params['mutation_rate'],
preservation_rate=options.ga_params['preservation_rate'],
exit_on_repeats=options.ga_params['exit_on_repeats'])
# Run N Times
now = time.time()
logging.debug('Solving with Genetic Algorithm')
population = ga.Run_Iterations(options.ga_params['max_iterations'])
ga_time = time.time() - now
# Log the Time
logging.info('GA Runtime: ' + str(ga_time) + ' seconds')
# Return results
return {
'time': ga_time,
'solution': population[0],
'population_size': len(population),
'selection_rate': options.ga_params['selection_rate']
}
len_train = len(train)
accept = 0
for inputs, correct in zip(train, target):
output = mlp.output(inputs)
if numpy.argmax(output) == numpy.argmax(correct):
accept += 1.0/len_train
return accept
ga = Genetic_Algorithm(individuals = 1000,
mutation_rate = 0.1,
# (4 caracteristicas + 1 do bias) * 2 neuronios = 10
# (2 saidas de neuronio + 1 do bias) * 3 neuronios de saida = 9
# total de pesos 10+9 = 19
genotype = 19,
fenotype = fenotype,
genotype_type = Genetic_Algorithm.TYPE_FLOAT)
# ga.print_individuals()
ga.execute(generations = 100, log=True)
# ga.print_individuals()
print "Best Individual for all generations", ga.get_best_individual().get_genotype(), ga.get_best_individual_fenotype()
genotype = ga.get_best_individual().get_genotype()
pos = 0
for i, layer in enumerate(mlp.get_layers()):
for j, neuron in enumerate(layer.get_neurons()):
accept = 0
for inputs, correct in zip(train, target):
output = mlp.output(inputs)
if numpy.argmax(output) == numpy.argmax(correct):
accept += 1.0 / len_train
return accept
ga = Genetic_Algorithm(
individuals=1000,
mutation_rate=0.1,
# (4 caracteristicas + 1 do bias) * 2 neuronios = 10
# (2 saidas de neuronio + 1 do bias) * 3 neuronios de saida = 9
# total de pesos 10+9 = 19
genotype=19,
fenotype=fenotype,
genotype_type=Genetic_Algorithm.TYPE_FLOAT)
# ga.print_individuals()
ga.execute(generations=100, log=True)
# ga.print_individuals()
print "Best Individual for all generations", ga.get_best_individual(
).get_genotype(), ga.get_best_individual_fenotype()
genotype = ga.get_best_individual().get_genotype()
pos = 0
'''
Created on 15/09/2015
@author: renan
'''
from ga.Genetic_Algorithm import Genetic_Algorithm
import numpy
def fenotype(individual):
return numpy.sum(individual.get_genotype())
if __name__ == '__main__':
ga = Genetic_Algorithm(individuals=4,
mutation_rate=0.1,
genotype=5,
fenotype=fenotype,
genotype_type=Genetic_Algorithm.TYPE_BINARY)
ga.print_individuals()
ga.execute(generations=10)
ga.print_individuals()
print "Best Individual for all generations", ga.get_best_individual().get_genotype(), ga.get_best_individual_fenotype()
