Question

我目前正在为python 3.x寻找一个成熟的GA库。但是，唯一可以找到的GA库是pyevolve和pygene。它们都只支持python 2.x.如果有人能提供帮助我会很感激。

Answer 1

DEAP：分布式进化算法支持Python 2和3： http://code.google.com/p/deap

免责声明：我是DEAP的开发人员之一。

Answer 2

不完全是GA库，但Clinton Sheppard的“遗传算法与Python”非常有用，因为它可以帮助您构建自己的GA库，满足您的需求。

Answer 3

这是一个不需要组装的软件包，可以用于任何问题：

https://pypi.org/project/geneticalgorithm/

Answer 4

检查PyGAD，这是一个用于实现遗传算法和训练机器学习算法的开源Python 3库。

可在“阅读文档”中找到该文档：https://pygad.readthedocs.io

通过pip安装：pip install pygad

这是一个使用PyGAD优化线性模型的示例。

import pygad
import numpy

"""
Given the following function:
    y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6
    where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=44
What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function.
"""

function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
desired_output = 44 # Function output.

def fitness_func(solution, solution_idx):
    # Calculating the fitness value of each solution in the current population.
    # The fitness function calulates the sum of products between each input and its corresponding weight.
    output = numpy.sum(solution*function_inputs)
    fitness = 1.0 / numpy.abs(output - desired_output)
    return fitness

fitness_function = fitness_func

num_generations = 100 # Number of generations.
num_parents_mating = 7 # Number of solutions to be selected as parents in the mating pool.

# To prepare the initial population, there are 2 ways:
# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.
# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.
sol_per_pop = 50 # Number of solutions in the population.
num_genes = len(function_inputs)

init_range_low = -2
init_range_high = 5

parent_selection_type = "sss" # Type of parent selection.
keep_parents = 7 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.

crossover_type = "single_point" # Type of the crossover operator.

# Parameters of the mutation operation.
mutation_type = "random" # Type of the mutation operator.
mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists or when mutation_type is None.

last_fitness = 0
def callback_generation(ga_instance):
    global last_fitness
    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
    print("Change     = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))

# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating, 
                       fitness_func=fitness_function,
                       sol_per_pop=sol_per_pop, 
                       num_genes=num_genes,
                       init_range_low=init_range_low,
                       init_range_high=init_range_high,
                       parent_selection_type=parent_selection_type,
                       keep_parents=keep_parents,
                       crossover_type=crossover_type,
                       mutation_type=mutation_type,
                       mutation_percent_genes=mutation_percent_genes,
                       callback_generation=callback_generation)

# Running the GA to optimize the parameters of the function.
ga_instance.run()

# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
ga_instance.plot_result()

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Parameters of the best solution : {solution}".format(solution=solution))
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))

prediction = numpy.sum(numpy.array(function_inputs)*solution)
print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))

if ga_instance.best_solution_generation != -1:
    print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))

# Saving the GA instance.
filename = 'genetic' # The filename to which the instance is saved. The name is without extension.
ga_instance.save(filename=filename)

# Loading the saved GA instance.
loaded_ga_instance = pygad.load(filename=filename)
loaded_ga_instance.plot_result()

Answer 5

嘿，这基本上是一个插件，但我想你们会喜欢的！

EasyGA 是一个 Python 包，旨在提供易于使用的遗传算法。该软件包旨在开箱即用，同时还允许您根据需要自定义功能。

这里是 wiki，它最擅长解释流程的工作原理。 https://github.com/danielwilczak101/EasyGA/wiki

这就是让它工作所需的一切：

pip3 install EasyGA

和一些示例代码：

import EasyGA

# Create the Genetic algorithm
ga = EasyGA.GA()

# Evolve the genetic algorithm
ga.evolve()

# Print your default genetic algorithm
ga.print_generation()
ga.print_population()

python 3.x的任何遗传算法模块？

5 个答案: