遗传算法选址问题matlab代码

由于遗传算法选址问题的具体实现涉及到具体的问题和数据，因此无法提供通用的matlab代码。以下是一个简单的遗传算法模板，供参考：

% 遗传算法模板
clear all;
close all;
clc;

% 参数设置
pop_size = 50; % 种群大小
gene_length = 10; % 染色体长度
max_generation = 100; % 最大迭代次数
pc = 0.8; % 交叉概率
pm = 0.1; % 变异概率
elite_count = 2; % 精英个体数量

% 初始化种群
pop = randi([0,1],pop_size,gene_length);

% 迭代
for generation = 1:max_generation
    % 计算适应度
    fitness = calculate_fitness(pop);
    % 选择
    selected_pop = selection(pop,fitness);
    % 交叉
    crossed_pop = crossover(selected_pop,pc);
    % 变异
    mutated_pop = mutation(crossed_pop,pm);
    % 精英策略
    elite_pop = elitism(pop,fitness,elite_count);
    % 合并种群
    pop = [elite_pop;mutated_pop];
end

% 计算最优解
best_fitness = 0;
best_individual = [];
for i = 1:size(pop,1)
    f = calculate_fitness(pop(i,:));
    if f > best_fitness
        best_fitness = f;
        best_individual = pop(i,:);
    end
end

% 输出结果
disp(['最优解：',num2str(best_individual)]);
disp(['最优适应度：',num2str(best_fitness)]);

% 适应度函数（此处为示例函数）
function f = calculate_fitness(individual)
    f = sum(individual);
end

% 选择函数
function selected_pop = selection(pop,fitness)
    % 轮盘赌选择
    p = fitness/sum(fitness);
    cum_p = cumsum(p);
    selected_pop = zeros(size(pop));
    for i = 1:size(pop,1)
        r = rand();
        for j = 1:size(pop,1)
            if r <= cum_p(j)
                selected_pop(i,:) = pop(j,:);
                break;
            end
        end
    end
end

% 交叉函数
function crossed_pop = crossover(pop,pc)
    crossed_pop = zeros(size(pop));
    for i = 1:2:size(pop,1)
        if rand() <= pc
            % 一点交叉
            pos = randi([1,size(pop,2)-1]);
            crossed_pop(i,:) = [pop(i,1:pos),pop(i+1,pos+1:end)];
            crossed_pop(i+1,:) = [pop(i+1,1:pos),pop(i,pos+1:end)];
        else
            crossed_pop(i,:) = pop(i,:);
            crossed_pop(i+1,:) = pop(i+1,:);
        end
    end
end

% 变异函数
function mutated_pop = mutation(pop,pm)
    mutated_pop = pop;
    for i = 1:size(pop,1)
        for j = 1:size(pop,2)
            if rand() <= pm
                % 每个基因以一定概率翻转
                mutated_pop(i,j) = ~pop(i,j);
            end
        end
    end
end

% 精英策略函数
function elite_pop = elitism(pop,fitness,count)
    [~,idx] = sort(fitness,'descend');
    elite_pop = pop(idx(1:count),:);
end

需要根据具体问题和数据，修改适应度函数、交叉函数、变异函数等。