使用 pyevolve 恢复优化

Question

我已经使用 Pyevolve 进行了优化，在查看结果后，我想添加几代以实现更好的收敛。由于评估相当长，我想知道是否可以将优化恢复到上一代，然后再添加大约 20 代。我希望一切都必须在数据库中设置，这样他才能成为可能。

这是我的 GA 属性（类似于第一个示例，但具有更复杂的评估函数）：

    # Genome instance, 1D List of 6 elements
genome = G1DList.G1DList(6)

# Sets the range max and min of the 1D List
genome.setParams(rangemin=1, rangemax=15)

# The evaluator function (evaluation function)
genome.evaluator.set(eval_func)

# Genetic Algorithm Instance
ga=GSimpleGA.GSimpleGA(genome)

# Set the Roulette Wheel selector method, the number of generations and
# the termination criteria
ga.selector.set(Selectors.GRouletteWheel)
ga.setGenerations(50)
ga.setPopulationSize(10)
ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)

# Sets the DB Adapter, the resetDB flag will make the Adapter recreate
# the database and erase all data every run, you should use this flag
# just in the first time, after the pyevolve.db was created, you can
# omit it.
sqlite_adapter = DBAdapters.DBSQLite(identify="F-Beam-Optimization", resetDB=True)
ga.setDBAdapter(sqlite_adapter)

# Do the evolution, with stats dump
# frequency of 5 generations
ga.evolve(freq_stats=2)

有人有这个想法吗？

Answer 1

您好，在查看了 Pyevolve 的文档后，似乎没有任何方法可以根据您存储在数据库中的内容恢复进化（奇怪的行为）。

如果你想实现这种类型的机制，你可以偶尔对你的人口进行一次腌制，然后在 Pyevolve 中实现整个过程。

或者，您可以尝试 DEAP 一个非常开放的框架，让您透明地查看和操作进化算法的各个方面。并且已经实现了一些检查点机制。

下面是您的代码在 DEAP 中的样子。

import random    
from deap import algorithms, base, creator, tools

# Create the needed types
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

# Container for the evolutionary tools
toolbox = base.Toolbox()
toolbox.register("attr", random.random, 1, 15)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr, 6)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

# Operator registering
toolbox.register("evaluate", eval_func)
toolbox.register("mate", tools.cxTwoPoints)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)

population = toolbox.population(n=10)
stats = tools.Statistics(key=lambda ind: ind.fitness.values)
stats.register("Max", max)
checkpoint = tools.Checkpoint(population=population)

GEN, CXPB, MUTPB = 0, 0.5, 0.1
while stats.Max() < CONDITION:
    # Apply standard variation (crossover followed by mutation)
    offspring = algorithms.varSimple(toolbox, population, cxpb=CXPB, mutpb=MUTPB)

    # Evaluate the individuals
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit

    # Select the fittest individuals
    offspring = [toolbox.clone(ind) for ind in toolbox.select(offspring, len(offspring)]
    # The "[:]" is important to not replace the label but what it contains
    population[:] = offspring

    stats.update(population)
    if GEN % 20 == 0:
        checkpoint.dump("my_checkpoint")
    GEN += 1

请注意，上述代码尚未经过测试。但它会满足您的所有要求。现在如何加载检查点并重新启动演化。

checkpoint = tools.Checkpoint()
checkpoint.load("my_checkpoint.ems")
population = checkpoint["population"]

# Continue the evolution has in before

此外，DEAP 的文档非常齐全，有超过 25 个多样化的示例，可以帮助新用户快速入门，我还听说开发人员回答问题的速度非常快。