matlab遗传算法

function [xv,fv] = myGA(fitness, a, b, NP, NG, Pc, Pm, eps)
%用遗传算法求解一维无约束优化问题
%
%待优化的目标函数 fitness
%自变量下界 a
%自变量上界 b
%种群个体数 NP
%最大进化代数 NG
%杂交概率　Pc
%变异概率 Pm
%自变量离散精度 eps
%目标变量取最大值时自变量的值: xm
%目标函数的最大值 fv
%
%Example:
%function F = fitness(x)
%F = x^3-60*x^2+900*x+100;
%   -------------------------------
%[xv,fv] = myGA(@fitness,0, 30, 50, 100, 0.9, 0.04, 0.01);
%--------------------------------------------------
%xv = 10
%fv = 4100
%
%本程序在《精通MATLAB最优化计算》页315程序的基础上修改L = ceil(log2((b-a) / eps + 1));%编码长度
x = zeros(NP, L);%种群
nx = zeros(size(x));%滚动数组
fx = zeros(NP, 1);%适应度
for i = 1:NP
x(i,:) = Initial(L);
endfv = -inf;for k = 1 : NG
for i = 1 : NP
fx(i) = fitness(Dec(a, b, x(i, :), L));
if (fx(i) > fv)
xv = Dec(a, b, x(i, :), L);
fv = fx(i);
end
endsumfx = sum(fx);
Px = fx / sumfx;PPx = zeros(NP, 1);
PPx(1) = Px(1);%概率叠加
for i = 2 : NP
PPx(i) = PPx(i - 1) + Px(i);
endselFather = 0;
for i = 1 : NP
sita = rand();
for j = 1 : NP
if (sita <= PPx(j))
selFather = j;%使用轮盘赌法进行选择父亲
break;
end
endselMother = floor(rand() * NP) + 1;%母亲随机选择
posCut = floor(rand() * (L - 1)) + 1;%交叉点r1 = rand();
if (r1 <= Pc)
nx(i, 1 : posCut) = x(selFather, 1:posCut);
nx(i, (posCut + 1) : L) = x(selMother, (posCut + 1) : L);
r2 = rand();
if (r2 <= Pm)
posMut = floor(rand() * L) + 1;
nx(i, posMut) = ~nx(i, posMut);
end
else
nx(i, :) = x(selFather, :);
end
endx = nx;
end%--------------------------------------------------------
%初始化种群
function  result = Initial(length)
result = zeros(size(length()));
for i = 1 : length
r = rand();
result(i) = round(r);
end%----------------------------------------------------------
%编码转换
function y = Dec(a, b, x, L)
base = 2 .^ ((L - 1) : -1: 0);
y = dot(base, x);
y = a + y * (b - a) / (2 ^ L - 1);