遗传算法代码

全局搜索最优算法（1）——遗传算法

pip3 install pygad 

在这里我们讨论一个简单的全局优化过程，讨论$(x-2{)}^2+(y-4{)}^2$在$x\subset (1,5),y\subset (6,9)$的最大值。
源码如下：

import numpy as np import pygad #the most important part in GA def fitness_func(solution, solution_idx): # solution[i] represeent the value of the (i+1)th parameter in solution fitness =pow((solution[0]-2),2)+pow((solution[1]-4),2) return fitness num_generations = 500 # Number of generations. num_parents_mating = 5 # 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 = 10 # Number of solutions in the population. parent_selection_type = "sss" # Type of parent selection. crossover_type = "single_point" # Type of the crossover operator. # Parameters of the mutation operation. mutation_type = "random" # Type of the mutation operator. 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)) last_fitness = ga_instance.best_solution()[1] # Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor. ga_instance = pygad.GA( #Number of generations(iterations). num_generations=50, num_parents_mating=num_parents_mating, fitness_func=fitness_func, sol_per_pop=sol_per_pop, #the Number of parameters(the number of genes) num_genes=2, # It is used to specify the possible values for each gene  # in case the user wants to restrict the gene values.  # It is useful if the gene space is restricted to  # a certain range or to discrete values.  # It accepts a list, tuple, or range.  # When all genes have the same global space,  # specify their values as a list/tuple/range.  # For example, gene_space = [0.3, 5.2, -4, 8]  # restricts the gene values to the 4 specified values.  # If each gene has its own space, then the gene_space # parameter can be nested like [[0.4, -5], [0.5, -3.2, 8.2, -9], ...]  # where the first sublist determines the values for  # the first gene,the second sublist for the second gene, and so on. gene_space=[np.arange(1, 5.01,0.01).tolist(),np.arange(6, 9.01,0.01).tolist()], parent_selection_type=parent_selection_type, # Number of parents to keep in the next population. # -1 means keep all parents # and 0 means keep nothing. keep_parents=1, crossover_type=crossover_type, mutation_type=mutation_type, # Percentage of genes to mutate. This parameter has no action # if the parameter mutation_num_genes exists. mutation_percent_genes=50, 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)) 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() 

1. 所有参数有一个范围或取值，如gene_space=[1,5,6],则表示所有参数只能取1,5,6中的一个值。
2. 每个参数有不同取值，如gene_space=[[1,6],[4,5,8]]，则表示第一个参数取值为1或者6，第二个参数取值为4，5或者8。

需要注意的是适应度函数应该返回的最优解对应的值，通过淘汰适应度低的物种，物竞天择，适者生存，最终返回全局最优解，这就是GA的整体流程。