def main():
pop = toolbox.population(n=Np)
CXPB, MUTPB, NGEN = 1, 1, Ngen
print("Start of evolution")
fitnesses = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
print("Evaluated %i individuals" % len(pop))
analizer = Analyzer()
for g in range(NGEN):
print("-- Generation %i --" % g)
elite = tools.selBest(pop, 1)
elite = list(map(toolbox.clone, elite))
offspring = toolbox.select(pop, len(pop))
offspring = list(map(toolbox.clone, offspring))
analizer.add_pop(list(map(toolbox.clone, offspring)))
record = stats.compile(offspring)
print(" Min %s" % record["min"])
print(" Max %s" % record["max"])
print(" Avg %s" % record["avg"])
print(" Std %s" % record["std"])
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CXPB:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
for mutant in offspring:
if random.random() < MUTPB:
toolbox.mutate(mutant)
del mutant.fitness.values
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
print("Evaluated %i individuals" % len(invalid_ind))
offspring = tools.selBest(offspring, len(offspring) - 1) + elite
pop[:] = offspring
print("-- End of (successful) evolution --")
best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
analizer.plot_entropy_matrix(file_name="SGA_entropy.png")
analizer.plot_stats(file_name="SGA_stats.png", optimum_val=1099)
print(mutpb)
return best_ind
def main():
pop = toolbox.population(n=Np)
# MUTPB：各世代で突然変異をさせる確率
MUTPB, NGEN = 1, Ngen
print("Start of evolution")
fitnesses = list(map(toolbox.evaluate, pop))
# 各個体の適応度を代入
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
print("Evaluated %i individuals" % len(pop))
analizer = Analyzer()
for g in range(NGEN):
print("-- Generation %i --" % g)
# エリートを選択
elite = tools.selBest(pop, 1)
elite = list(map(toolbox.clone, elite))
# 新しい演算子により交叉
offspring = toolbox.crossover(pop)
# 突然変異
gen = pop + offspring # 2Np
for mutant in gen:
if random.random() < MUTPB:
toolbox.mutate(mutant)
del mutant.fitness.values
gen += elite
invalid_ind = [ind for ind in gen if not ind.fitness.valid]
print("Evaluated %i individuals" % len(invalid_ind))
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
selected = toolbox.select(gen, k=Np)
pop[:] = selected
record = stats.compile(pop)
print(" Min %s" % record["min"])
print(" Max %s" % record["max"])
print(" Avg %s" % record["avg"])
print(" Std %s" % record["std"])
analizer.add_pop(list(map(toolbox.clone, pop)))
print("-- End of (successful) evolution --")
best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
analizer.plot_entropy_matrix(file_name="TDGA_newcross_entropy.png")
analizer.plot_stats(file_name="TDGA_newcross_stats.png", optimum_val=1099)
return best_ind
def main():
pop = toolbox.population(n=Np)
CXPB, MUTPB, NGEN = 1, 1, Ngen
print("Start of evolution")
fitnesses = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
print("Evaluated %i individuals" % len(pop))
analizer = Analyzer()
for g in range(NGEN):
print("-- Generation %i --" % g)
elite = tools.selBest(pop, 1)
elite = list(map(toolbox.clone, elite))
offspring = list(map(toolbox.clone, pop))
for i in range(len(offspring)):
p1_idx, p2_idx= parents_select(offspring, tournament_size)
ind1 = offspring[p1_idx]
ind2 = offspring[p2_idx]
if random.random() < CXPB:
toolbox.mate(ind1, ind2)
gen = pop + offspring # 2Np
for mutant in gen:
if random.random() < MUTPB:
toolbox.mutate(mutant)
del mutant.fitness.values
gen += elite
invalid_ind = [ind for ind in gen if not ind.fitness.valid]
print("Evaluated %i individuals" % len(invalid_ind))
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
selected = toolbox.select(gen, k=Np)
pop[:] = selected
record = stats.compile(pop)
print(" Min %s" % record["min"])
print(" Max %s" % record["max"])
print(" Avg %s" % record["avg"])
print(" Std %s" % record["std"])
analizer.add_pop(list(map(toolbox.clone, pop)))
print("-- End of (successful) evolution --")
best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
analizer.plot_entropy_matrix(file_name="TDGA_entropy.png")
analizer.plot_stats(file_name="TDGA_stats.png", optimum_val=1099)
return best_ind
