def main(n_corr, p):
energy_coolng(n_corr, p)
pop_size=500
#toolbox.register("select",selElitistAndTournament, k_elitist=int(0.1*pop_size), k_tournament=pop_size - int(0.1*pop_size), tournsize=3)
toolbox.register("select",tools.selTournament, tournsize=3)
toolbox.register("mate", gp.cxOnePoint)
toolbox.register("expr_mut", gp.genFull, min_=0, max_=3)
toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
toolbox.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
pop = toolbox.population(n=pop_size)
hof = tools.HallOfFame(3)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", np.mean)
mstats.register("std", np.std)
mstats.register("min", np.min)
mstats.register("max", np.max)
cxpb = 0.7
mutpb = 0.3
ngen = 100
params = ['best_of_each_specie', 2, 'yes']
neat_cx = True
neat_alg = True
neat_pelit = 0.5
neat_h = 0.15
problem = "EnergyCooling"
pop, log = eaneatGP.neat_GP(pop, toolbox, cxpb, mutpb, ngen, neat_alg, neat_cx, neat_h, neat_pelit, n_corr, p, params, problem, stats=mstats, halloffame=hof, verbose=True)
#pop, log = algorithms.eaSimple(pop, toolbox, cxpb, mutpb, ngen, problem, p, n_corr,stats=mstats, halloffame=hof, verbose=True)
return pop, log, hof
def main(n_corr, num_p):
problem = "BreastCancer"
name_database = "breast-cancer-wisconsin"
pop_size = 100
energy_coolng(n_corr, num_p, problem, name_database)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("mate", neat_gp.cxSubtree)
toolbox.register("expr_mut", gp.genFull, min_=0, max_=3)
toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
toolbox.decorate(
"mate", gp.staticLimit(key=operator.attrgetter("height"),
max_value=17))
toolbox.decorate(
"mutate",
gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
pop = toolbox.population(n=pop_size)
hof = tools.HallOfFame(3)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", np.mean)
mstats.register("std", np.std)
mstats.register("min", np.min)
mstats.register("max", np.max)
cxpb = 0.7
mutpb = 0.3
ngen = 100
params = ['best_of_each_specie', 2, 'yes']
neat_cx = True
neat_alg = True
neat_pelit = 0.5
neat_h = 0.15
neat_beta = 0.5
pop, log = eaneatGP.neat_GP(pop,
toolbox,
cxpb,
mutpb,
ngen,
neat_alg,
neat_cx,
neat_h,
neat_pelit,
n_corr,
num_p,
params,
problem,
neat_beta,
stats=mstats,
halloffame=hof,
verbose=True)
return pop, log, hof
def main(n_corr, num_p):
problem = "EnergyCooling"
name_database="energy_efficiency_Cooling"
pop_size = 500
energy_coolng(n_corr, num_p, problem, name_database)
toolbox.register("select",tools.selTournament, tournsize=3)
toolbox.register("mate", neat_gp.cxSubtree)
toolbox.register("expr_mut", gp.genFull, min_=0, max_=3)
toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
toolbox.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
pop = toolbox.population(n=pop_size)
hof = tools.HallOfFame(3)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", np.mean)
mstats.register("std", np.std)
mstats.register("min", np.min)
mstats.register("max", np.max)
cxpb = 0.7
mutpb = 0.3
ngen = 100
params = ['best_of_each_specie', 2, 'yes']
neat_cx = False
neat_alg = False
neat_pelit = 0.5
neat_h = 0.15
pop, log = eaneatGP.neat_GP(pop, toolbox, cxpb, mutpb, ngen, neat_alg, neat_cx, neat_h, neat_pelit, n_corr, num_p, params, problem, stats=mstats, halloffame=hof, verbose=True)
return pop, log, hof
def main(n_corr, p, problem, database_name, pset, config):
pop_size = config["population_size"]
cxpb = config["cxpb"] # 0.9
mutpb = config["mutpb"] # 0.1
tournament_size = config["tournament_size"]
ngen = config["generations"]
params = ['best_of_each_specie', 2, 'yes']
neat_cx = config["neat_cx"]
neat_alg = config["neat_alg"]
neat_pelit = config["neat_pelit"]
neat_h = config["neat_h"]
beta = config["neat_beta"]
creator.create("FitnessMin", base.Fitness, weights=(-1.0, ))
creator.create("FitnessTest", base.Fitness, weights=(-1.0, ))
creator.create("Individual",
neat_gp.PrimitiveTree,
fitness=creator.FitnessMin,
fitness_test=creator.FitnessTest)
toolbox = base.Toolbox()
if neat_cx:
toolbox.register("expr", gp.genFull, pset=pset, min_=0, max_=3)
else:
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=0, max_=7)
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.expr)
toolbox.register("population", init_conf.initRepeat, list,
toolbox.individual)
toolbox.register("compile", gp.compile, pset=pset)
name_database = database_name
direccion = "./data_corridas/%s/train_%d_%d.txt"
train_test(n_corr, p, problem, name_database, toolbox, config)
toolbox.register("select", tools.selTournament, tournsize=tournament_size)
toolbox.register("mate", neat_gp.cxSubtree)
if neat_cx:
toolbox.register("expr_mut", gp.genFull, min_=0, max_=3)
else:
toolbox.register("expr_mut", gp.genHalfAndHalf, min_=0, max_=7)
toolbox.register("mutate",
neat_gp.mutUniform,
expr=toolbox.expr_mut,
pset=pset)
toolbox.decorate(
"mate", gp.staticLimit(key=operator.attrgetter("height"),
max_value=17))
toolbox.decorate(
"mutate",
gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
pop = toolbox.population(n=pop_size)
hof = tools.HallOfFame(3)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", np.mean)
mstats.register("std", np.std)
mstats.register("min", np.min)
mstats.register("max", np.max)
pop, log = eaneatGP.neat_GP(pop,
toolbox,
cxpb,
mutpb,
ngen,
neat_alg,
neat_cx,
neat_h,
neat_pelit,
n_corr,
p,
params,
problem,
beta,
stats=mstats,
halloffame=hof,
verbose=True)
return pop, log, hof