遗传算法完整MATLAB程序（编码，轮盘赌法）

% 对个体按适应度大小进行排序，并且保存最佳个体 % population_size: 种群大小 % chromosome_size: 染色体长度 function rank(population_size, chromosome_size) global fitness_value; % 种群适应度 global fitness_sum; % 种群累计适应度 global fitness_average; global best_fitness; global best_individual; global best_generation; global population; global G; for i=1:population_size fitness_sum(i) = 0.; end min_index = 1; temp = 1; temp_chromosome(chromosome_size)=0; % 遍历种群 % 冒泡排序 % 最后population(i)的适应度随i递增而递增，population(1)最小，population(population_size)最大 for i=1:population_size min_index = i; for j = i+1:population_size if fitness_value(j) < fitness_value(min_index); min_index = j; end end if min_index ~= i % 交换 fitness_value(i) 和 fitness_value(min_index) 的值 temp = fitness_value(i); fitness_value(i) = fitness_value(min_index); fitness_value(min_index) = temp; % 此时 fitness_value(i) 的适应度在[i,population_size]上最小 % 交换 population(i) 和 population(min_index) 的染色体串 for k = 1:chromosome_size temp_chromosome(k) = population(i,k); population(i,k) = population(min_index,k); population(min_index,k) = temp_chromosome(k); end end end % fitness_sum(i) = 前i个个体的适应度之和 for i=1:population_size if i==1 fitness_sum(i) = fitness_sum(i) + fitness_value(i); else fitness_sum(i) = fitness_sum(i-1) + fitness_value(i); end end % fitness_average(G) = 第G次迭代 个体的平均适应度 fitness_average(G) = fitness_sum(population_size)/population_size; % 更新最大适应度和对应的迭代次数，保存最佳个体(最佳个体的适应度最大) if fitness_value(population_size) > best_fitness best_fitness = fitness_value(population_size); best_generation = G; for j=1:chromosome_size best_individual(j) = population(population_size,j); end end clear i; clear j; clear k; clear min_index; clear temp; clear temp1;