def main():
# Differential evolution parameters
CR = 0.25
F = 1
MU = 15
NGEN = 20
pop = toolbox.population(n=MU);
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
record = stats.compile(pop)
logbook.record(gen=0, evals=len(pop), **record)
#print(logbook.stream)
for g in range(1, NGEN):
for k, agent in enumerate(pop):
a,b,c = toolbox.select(pop)
y = toolbox.clone(agent)
index = random.randrange(NDIM)
for i, value in enumerate(agent):
if i == index or random.random() < CR:
y[i] = a[i] + F*(b[i]-c[i])
y.fitness.values = toolbox.evaluate(y)
if y.fitness > agent.fitness:
pop[k] = y
hof.update(pop)
record = stats.compile(pop)
logbook.record(gen=g, evals=len(pop), **record)
print(logbook.stream)
print("Best individual is ", hof[0], hof[0].fitness.values[0])
train(hof[0], True)
def main():
# Differential evolution parameters
CR = 0.25
F = 1
MU = 15
NGEN = 20
pop = toolbox.population(n=MU)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
record = stats.compile(pop)
logbook.record(gen=0, evals=len(pop), **record)
#print(logbook.stream)
for g in range(1, NGEN):
for k, agent in enumerate(pop):
a, b, c = toolbox.select(pop)
y = toolbox.clone(agent)
index = random.randrange(NDIM)
for i, value in enumerate(agent):
if i == index or random.random() < CR:
y[i] = a[i] + F * (b[i] - c[i])
y.fitness.values = toolbox.evaluate(y)
if y.fitness > agent.fitness:
pop[k] = y
hof.update(pop)
record = stats.compile(pop)
logbook.record(gen=g, evals=len(pop), **record)
print(logbook.stream)
print("Best individual is ", hof[0], hof[0].fitness.values[0])
train(hof[0], True)
def evaluate_nn(individual):
if individual[0] < 1 or individual[1] < 1 or individual[1] < 1:
return 10000,
return train(individual)[0],
def evaluate_nn(individual):
if individual[0] < 1 or individual[1] < 1 or individual[1] < 1:
return 10000,
return train(individual)[0],
