def search_ga(max_gens, pop_size, ad_mut_stp, mu_lambda):
generation = 0
population = init_population(pop_size)
best = fitness_func(population)
while generation < max_gens:
new_population = mutate_random(tuple(population))
new_best = fitness_func(new_population)
if new_best <= best:
LOG.debug("Better population>{0}".format(new_population))
population = new_population
best = new_best
LOG.rbf("Generation>{0}:new best>{1}".format(generation, best))
generation = generation + 1
def search_es(max_gens, pop_range, ad_mut_stp, mu_lambda):
pop = init_population(pop_range)
best = fitness_func(pop[0])
p = 1.5
for gen in range(0, max_gens-1):
children = mutate(pop[0], pop[1], p)
LOG.debug("children>{0}".format(children))
fitness = fitness_func(children[0])
if fitness <= best:
best = fitness
pop = children
p = 1.5
else:
p = 1.5 ** (-1/4)
if mu_lambda:
pop = init_population(pop_range)
best = fitness_func(pop[0])
LOG.rbf("Generation>{0}:new best>{1}".format(gen, best))
return best
def search_es(max_gens, pop_range, ad_mut_stp, mu_lambda):
pop = init_population(pop_range)
best = fitness_func(pop[0])
p = 1.5
for gen in range(0, max_gens - 1):
children = mutate(pop[0], pop[1], p)
LOG.debug("children>{0}".format(children))
fitness = fitness_func(children[0])
if fitness <= best:
best = fitness
pop = children
p = 1.5
else:
p = 1.5**(-1 / 4)
if mu_lambda:
pop = init_population(pop_range)
best = fitness_func(pop[0])
LOG.rbf("Generation>{0}:new best>{1}".format(gen, best))
return best
