def main():
population = 100
variables = 5
generations = 200
constraints = gacommon.make_constraints_array(variables, -5, +5)
kursawe_fn = lambda g: test_problems.kursawe(np.asarray(g))
genome = [constraints[:,0] + (constraints[:,1]-constraints[:,0])
* np.random.random(variables) for i in range(population)]
alg = spea2.Spea2(genome)
im = island_model.IslandModel(alg, percentage=.2)
im.start(
kursawe_fn, lambda : alg.generation < generations
, gacommon.blxa_crossover
, lambda s: gacommon.is_within_constraints(s, constraints)
, lambda s: gacommon.mutate_uniform(s, constraints, prob=.02)
)
np.savez("kursawe_data_" + str(MPI.COMM_WORLD.rank) + ".npz"
, genome = im.island.genome, ospace = im.island.ospace
, fitnesses = im.island.fitnesses)
def main():
population = 100
variables = 5
generations = 200
constraints = gacommon.make_constraints_array(variables, -5.12, +5.12)
rastrigin_fn = lambda g: test_problems.rastrigin_6(np.asarray(g))
genome = [constraints[:,0] + (constraints[:,1]-constraints[:,0])
* np.random.random(variables) for i in range(population)]
alg = sga.SGA(genome)
im = island_model.IslandModel(alg, percentage=.2)
fittest = im.start(
rastrigin_fn, lambda : alg.generation < generations
, gacommon.blxa_crossover
, lambda s: gacommon.is_within_constraints(s, constraints)
, lambda s: gacommon.mutate_uniform(s, constraints, prob=.02)
)
with open("rastrigin_data_" + str(MPI.COMM_WORLD.rank), "w") as f:
for v in fittest[0]:
f.write(str(v) + " ")
f.write(str(fittest[1]))