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);

end



fv = -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

	end

	

	sumfx = 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);

	end

	

	selFather = 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

		end

		

		selMother = 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

	end

	

	x = 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);