def ea(SIGMA=SIGMA):
# initialize
world = World(fitness=fitness)
world.population = IntListPopulation(length=MU, gene_size=GENE_SIZE)
# iterate over generations
for generation_count in range(MAX_GENERATIONS):
# log this generation's population to stdout
print("%s: %s" % (generation_count, world.population))
# if we reached a sufficiently good solution, stop
if min(world.scores) < MIN_FITNESS:
break
# select rho fittest individuals as parents
parents = select(world=world, rho=RHO)
# generate children by crossing over the parents
children_original = []
while len(children_original) < LAMBDA:
children_original += crossover(population=parents)
# mutate children
children = [mutate(individual=individual, mu=0, sigma=SIGMA)
for individual in children_original]
# self-adapt mutation strenght using Rechenberg's success rule
SIGMA = rechenberg(children_original=children_original,
children_mutated=children, fitness=world.fitness,
a=A_RECHENBERG, sigma=SIGMA)
if MODE == '+':
# extend population with children
world.population += Population.from_individuals(
individuals=children)
elif MODE == ',':
# replace population with children
world.population = Population.from_individuals(
individuals=children)
# select fittest mu individuals for next generation
world.population = select(rho=MU, world=world)
# log best individual
print("Best individual is %s with a score of %s." %
(select(world=world, rho=1)[0], min(world.scores)))
def test():
WORLD = World(fitness=fitness)
WORLD.population = IntListPopulation(length=10)
rho = 2
PARENTS = select_fittest(WORLD, rho=rho)
assert len(PARENTS) == rho
assert PARENTS[0].gene[0] == 0
assert PARENTS[1].gene[0] == 0