给出一段matalab语言开发的遗传算法

以下是一个简单的 Matlab 遗传算法的示例代码：

% 定义遗传算法的参数
popSize = 100; % 种群大小
nGenes = 20; % 基因数量
mutationRate = 0.01; % 变异率
crossoverRate = 0.8; % 交叉率
nGenerations = 50; % 迭代次数

% 初始化种群
population = randi([0 1], popSize, nGenes);

% 迭代遗传算法
for i = 1:nGenerations
% 计算每个个体的适应度
fitness = sum(population, 2);

% 选择父母个体
parent1 = rouletteSelection(population, fitness);
parent2 = rouletteSelection(population, fitness);

% 进行交叉操作
offspring = crossover(parent1, parent2, crossoverRate);

% 进行变异操作
offspring = mutation(offspring, mutationRate);

% 合并父母和后代
population = [parent1; parent2; offspring];

% 保持种群大小不变
population = population(1:popSize, :);
end

% 打印最优个体
bestIndividual = population(find(fitness == max(fitness)), :);
disp(['Best individual: ' num2str(bestIndividual)]);

% 选择操作
function parent = rouletteSelection(population, fitness)
% 计算每个个体的选择概率
p = fitness / sum(fitness);

% 根据概率选择父母个体
cumprob = cumsum(p);
r = rand();
for i = 1:size(population, 1)
if r < cumprob(i)
parent = population(i, :);
break;
end
end
end

% 交叉操作
function offspring = crossover(parent1, parent2, crossoverRate)
% 根据交叉率决定是否进行交叉操作
if rand() < crossoverRate
% 随机选择交叉点
crossoverPoint = randi([1 size(parent1, 2)]);

% 进行单点交叉操作
offspring = [parent1(1:crossoverPoint) parent2(crossoverPoint+1:end)];
else
% 未进行交叉操作，直接复制父母基因
offspring = parent1;
end
end

% 变异操作
function offspring = mutation(individual, mutationRate)
% 根据变异率决定是否进行变异操作
if rand() < mutationRate
% 随机选择要进行变异的基因
mutationPoint = randi([1 size(individual, 2)]);

% 变异操作，将该基因取反
individual(mutationPoint) = ~individual(mutationPoint);
end

offspring = individual;
end