def double_population(self):
for _ in xrange(len(self.solution_pool)):
parent1 = random.choice(self.solution_pool)
parent2 = random.choice(self.solution_pool)
child = Solution.cross_over(parent1, parent2)
should_mutate = random.random() < (1.0 / len(self.solution_pool))
if should_mutate:
child.mutate(1)
child.repair()
self.add_solution(child)
def main():
parser = argparse.ArgumentParser(
description='Poorly solve the general assignment problem.',
fromfile_prefix_chars='@')
parser.add_argument('filename',
metavar='filename',
type=unicode,
help='a data file to parse and run on')
parser.add_argument('--population',
metavar='population',
type=int,
default=100,
help='how large to keep population')
parser.add_argument('--evaluations',
metavar='evaluations',
type=int,
default=100000,
help='how many evaluations to run between restarts')
parser.add_argument('--restarts',
metavar='restarts',
type=int,
default=10,
help='how often to restart from a random point')
args = parser.parse_args()
POPULATION = args.population
NUMBER_OF_EVALUATIONS = args.evaluations
RESTARTS = args.restarts
total_evaluations = 0
top_genotypes = []
for n in xrange(RESTARTS):
print "restart", n, "out of {0}:".format(RESTARTS)
print
with open(args.filename) as f:
ga = GeneticAlgorithm.from_file(f, fittest_found_callback)
ga.generate_random_solutions(POPULATION)
evolutions = NUMBER_OF_EVALUATIONS / POPULATION
for _ in xrange(evolutions):
ga.evolve_population()
indexes = agent_indexes(ga.agents)
top_genotypes.extend(
assignment_indexes(solution) for solution in ga.solution_pool)
total_evaluations += ga.evaluations
with open(args.filename) as f:
ga = GeneticAlgorithm.from_file(f, fittest_found_callback)
ga.solution_pool = [
Solution(ga.agents, assignment_agents(genotype, ga.agents))
for genotype in top_genotypes
]
while len(ga.solution_pool) > 10:
ga.double_population()
ga.halve_population()
ga.halve_population()
print "population:", len(ga.solution_pool)
print "all done:"
print "total_evaluations: ", (ga.evaluations + total_evaluations)
best_solution = sorted(ga.solution_pool, key=lambda s: s.total_cost)[0]
fittest_found_callback(best_solution, ga)
def generate_random_solutions(self, population_size):
for _ in xrange(population_size):
solution = Solution.generate_random(self.agents)
self.add_solution(solution)
