自适应遗传算法matlab代码
时间: 2023-10-11 07:12:44 浏览: 56
以下是一个简单的自适应遗传算法的MATLAB实现代码,仅供参考:
```matlab
% 初始化种群
popSize = 50; % 种群大小
nVars = 10; % 自变量个数
pop = rand(popSize, nVars); % 随机产生初始种群
% 设置遗传算法参数
maxGen = 100; % 最大迭代次数
pc = 0.8; % 交叉概率
nc = 2; % 交叉点个数
pm = 0.01; % 变异概率
nm = 1; % 变异个数
% 计算种群适应度
fitness = zeros(popSize, 1);
for i = 1:popSize
fitness(i) = objectiveFunction(pop(i, :));
end
% 进化过程
for gen = 1:maxGen
% 计算种群适应度
fitness = zeros(popSize, 1);
for i = 1:popSize
fitness(i) = objectiveFunction(pop(i, :));
end
% 计算适应度比例
totalFitness = sum(fitness);
p = fitness / totalFitness;
% 计算平均适应度和适应度标准差
meanFitness = mean(fitness);
stdFitness = std(fitness);
% 计算选择概率
a = 1 / (2 * log(nVars)); % 参数a
b = exp(1) / nVars; % 参数b
q = a * log(p / b) + 1;
q = q / sum(q);
% 选择新的种群
newPop = zeros(popSize, nVars);
for i = 1:popSize
% 选择两个个体进行交叉
k1 = rouletteWheel(q);
k2 = rouletteWheel(q);
if rand() <= pc
[c1, c2] = crossover(pop(k1, :), pop(k2, :), nc);
else
c1 = pop(k1, :);
c2 = pop(k2, :);
end
% 变异
if rand() <= pm
c1 = mutation(c1, nm);
end
if rand() <= pm
c2 = mutation(c2, nm);
end
% 添加到新种群中
newPop(i, :) = c1;
newPop(i+1, :) = c2;
end
% 更新种群
pop = newPop;
end
% 最优解
[~, idx] = min(fitness);
bestSol = pop(idx, :);
bestFitness = fitness(idx);
% 目标函数
function f = objectiveFunction(x)
% TODO: 目标函数
end
% 轮盘赌选择
function k = rouletteWheel(p)
r = rand();
c = cumsum(p);
k = find(r <= c, 1, 'first');
end
% 交叉
function [c1, c2] = crossover(p1, p2, nc)
nVars = length(p1);
c1 = p1;
c2 = p2;
for i = 1:nc
k = randi(nVars);
c1(k:end) = p2(k:end);
c2(k:end) = p1(k:end);
end
end
% 变异
function c = mutation(p, nm)
nVars = length(p);
c = p;
for i = 1:nm
k = randi(nVars);
c(k) = rand();
end
end
```
需要根据具体问题进行修改,包括目标函数、变量范围、适应度函数等。