代码例如以下：
function m_main()
clear
clc
Max_gen = 100;% 执行代数
pop_size = 100;%种群大小
chromsome = 10;%染色体的长度
pc = 0.9;%交叉概率
pm = 0.25;%变异概率
gen = 0;%统计代数
%初始化
init = 40*rand(pop_size, chromsome)-20;
pop = init;
fit = obj_fitness(pop);
[max_fit, index_max] = max(fit);
maxfit = max_fit;
[min_fit, index_min] = min(fit);
best_indiv = pop(index_max, :);
%迭代操作
while gen<Max_gen
gen = gen+1;
bt(gen) = max_fit;
if maxfit<max_fit;
maxfit = max_fit;
pop(index_min, :) = pop(index_max, :);
best_indiv = pop(index_max, :);
end
best_indiv_tmp(gen) = pop(index_max);
newpop = ga(pop, pc, pm, chromsome, fit);
fit = obj_fitness(newpop);
[max_fit, index_max] = max(fit);
[min_fit, index_min] = min(fit);
pop = newpop;
trace(1, gen) = max_fit;
trace(2, gen) = sum(fit)./length(fit);
end
%执行结果
[f_max gen_ct] = max(bt)%求的最大值以及代数
maxfit
best_indiv
%绘图
% bt
hold on
plot(trace(1, :), '.g:');
plot( trace(2, :), '.r-');
title('实验结果图')
xlabel('迭代次数/代'), ylabel('最佳适应度（最大值）');%坐标标注
plot(gen_ct-1, 0:0.1:f_max+1, 'c-');%画出最大值
text(gen_ct, f_max+1, '最大值')
hold off
function [fitness] = obj_fitness(pop)
%适应度计算函数
[r c] = size(pop);
x = pop;
fitness = zeros(r, 1);
for i = 1:r
for j = 1:c
fitness(i, 1) = fitness(i, 1)+sin(sqrt(abs(40*x(i))))+1-abs(x(i))/20.0;
end
end
end
function newpop = ga(pop, pc, pm, chromsome, fit)
pop_size = size(pop, 1);
%轮盘赌选择
ps = fit/sum(fit);
pscum = cumsum(ps);%size(pscum)
r = rand(1, pop_size);
qw = pscum*ones(1, pop_size);
selected = sum(pscum*ones(1, pop_size)<ones(pop_size, 1)*r)+1;
newpop = pop(selected, :);
%交叉
if pop_size/2 ~= 0
pop_size = pop_size-1;
end
for i = 1:2:pop_size-1
while pc>rand
c_pt = round(8*rand+1);
pop_tp1 = newpop(i, :);pop_tp2 = newpop(i+1, :);
newpop(i+1, 1:c_pt) = pop_tp1(1, 1:c_pt);
newpop(i, c_pt+1:chromsome) = pop_tp2(1, c_pt+1:chromsome);
end
end
% 变异
for i = 1:pop_size
if pm>rand
m_pt = 1+round(9*rand);
newpop(i, m_pt) = 40*rand-20;
end
end
end
end
