基于遗传算法的随机规划（matlab）

function pop = crossover(pop,g_para) pop_size = g_para.pop_size; chromosome = pop.chromosome; for m = 1:pop_size if rand() < g_para.crossover_probability/2 crossover_label1 = randi(pop_size); crossover_label2 = randi(pop_size); meet_constraint_flag = 0; while meet_constraint_flag == 0 c = rand(1,g_para.chromosome_size); new_chromosome1 = c.*chromosome(crossover_label1,:)... +(1-c).*chromosome(crossover_label2,:); new_chromosome2 = c.*chromosome(crossover_label2,:)... +(1-c).*chromosome(crossover_label1,:); meet_constraint_flag = all([isMeetConstraint(new_chromosome1),... isMeetConstraint(new_chromosome2)]); end chromosome(crossover_label1,:) = new_chromosome1; chromosome(crossover_label2,:) = new_chromosome2; end end pop.chromosome = chromosome; end