我正在尝试对几何模型进行符号回归。而且它在大多数时候都因健身得分不接近0而卡住。因此，我进行了一些研究，发现这是局部极小值的问题。一些人试图将人口多样性优先于健康。但这不是我想要的。
所以我要做的是重新配置guard let selectedImage = info[.editedImage] as? UIImage else {
}
并在其中添加一个块。因此，当最后algorithms.eaSimple代的适应性相同时，它将重置总体。
除此之外，我没有其他想法，因为我很陌生。
还有更好的方法吗？
我正在使用健身功能。 n=50
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))def my_eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None, halloffame: tools.HallOfFame = None,
verbose=True):
logbook = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
gen = 1
last_few_pop_to_consider = 50
starting_condition = last_few_pop_to_consider
is_last_few_fitness_same = lambda stats_array: abs(numpy.mean(stats_array) - stats_array[0]) < 0.1
while gen < ngen + 1:
# Select the next generation individuals
offspring = toolbox.select(population, len(population))
# Vary the pool of individuals
offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Replace the current population by the offspring
population[:] = offspring
# Append the current generation statistics to the logbook
record = stats.compile(population) if stats else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
gen += 1
# stopping criteria
min_fitness = record['fitness']['min\t']
# max_fitness = record['fitness']['max\t']
if min_fitness < 0.1:
print('Reached desired fitness')
break
if gen > starting_condition:
min_stats = logbook.chapters['fitness'].select('min\t')[-last_few_pop_to_consider:]
if is_last_few_fitness_same(min_stats):
print('Defining new population')
population = toolbox.population(n=500)
starting_condition = gen + last_few_pop_to_consider
return population, logbook
在第59代中获得504.4395的最低适应度后，人口得到重置。