我想在matlab中找到遗传算法的最小函数值(我知道matlab有GA的工具箱,但我希望它能以程序化的方式实现)。 我有四个m文件,Itterate 50次,并且在每个循环步骤中保存最佳和意味着的适应性,但是当我运行此代码时,不会返回我的较低值最好和平均,这是不正常的。我的问题在哪里?
我的数学函数是找到 f(x)= - | x * sin(sqrt(| x |))|
的minmun的main.m
global population;
global fitness;
global popsize;
format bank;
popsize=50;
report=zeros(popsize,2);
selected=ones(1,50);
fitness=zeros(1,50);
population = randi([0 1], 50, 10);
for j=1:popsize
calFitness();
for i=1:popsize
selected(1,i)=(rol_wheel(fitness));
end;
population =recombin(population,selected);
report(j,:)=[min(fitness),mean(fitness)];
end
calFitness
function [] = calFitness( )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
global population;
global fitness;
global popsize;
%population=population.*2;
for i=1:popsize
x=bin2dec(num2str(population(i,:)))/2;
fitness(1,i)= abs(x*sin(sqrt(abs(x))));
%disp(fitness);
end
%disp();
rol_wheel
% ---------------------------------------------------------
% Roulette Wheel Selection Algorithm. A set of weights
% represents the probability of selection of each
% individual in a group of choices. It returns the index
% of the chosen individual.
% Usage example:
% fortune_wheel ([1 5 3 15 8 1])
% most probable result is 4 (weights 15)
% ---------------------------------------------------------
function choice = rol_wheel(weights)
accumulation = cumsum(weights);
p = rand() * accumulation(end);
chosen_index = -1;
for index = 1 : length(accumulation)
if (accumulation(index) > p)
chosen_index = index;
break;
end
end
%keyboard
choice = chosen_index;
recombin
function pop = recombin( popu,selected )
global popsize;
pop=zeros(50,10);
for i=1:popsize/2
rc=randi([1,10]);
for j=1:10
pop(i,1:rc-1)=popu(selected(i),1:rc-1);
pop(i,rc:end)=popu(selected(i+25),rc:end);
pop(i+25,1:rc-1)=popu(selected(i+25),1:rc-1);
pop(i+25,rc:end)=popu(selected(i),rc:end);
%keyboard
end
end
end
我将不胜感激任何答复和帮助。