本段落提供了一套完整的MATLAB代码，利用遗传算法解决不重复整数序列的优化排序问题，适用于需要高效搜索和排序策略的研究与工程应用。
%% 遗传算法主循环
% 显示进度条
waitbar_handle = waitbar(0,遗传算法运行中...);
while generation < max_generation
%% 应用遗传算子
fitness_values = ranking(fitness_value); % 分配适应度值给个体
population = selection(rws, population, fitness_values, 1); % 种群选择操作
population = mutation(population, pop_size, PM, N); % 对种群进行变异，单点变异方式
population = crossover(population, pop_size, PC, N); % 执行交叉操作，两点交叉方法
decoded_value = decoding_function(population,pop_size);% 解码染色体
end
close(waitbar_handle);
%% 辅助函数定义
function fitness_values = ranking(fitness)
% 对适应度值进行排名
fitness_ranked = sortrows([fitness, 1:numel(fitness)]);
fitness_values = interp1(unique(sort(fitness)), linspace(0, 1, numel(unique(sort(fitness)))), fitness);
end
function population_new = selection(rws_method,population_fitness,fv,num_selected)
% 根据适应度值从当前种群中选择个体
switch rws_method
case rws
% 轮盘赌选择方法实现
end
function mutated_population = mutation(population, pop_size, PMutationRate,NVariables)
% 对染色体进行变异操作，单点变异方式
for i=1:pop_size
if rand < PMutationRate
point=randi(NVariables);
population(i,point)=randperm(NVariables); % 单个基因位的值被随机置换为新的不重复整数排序编码
end
end
mutated_population = population;
function crossed_population = crossover(population,pop_size,CrossoverProbability,N)
% 实现两点交叉操作，用于生成新种群
for i=1:2:pop_size-1
if rand < CrossoverProbability
pointA=randi(N);
pointB=(pointA+randi([0 N-pointA])) % 确定两个随机点以进行片段交换
temp = population(i,pointA:pointB);
population(i,pointA:pointB) = population(i+1,pointA:pointB);
population(i+1,pointA:pointB)=temp;
end
end
crossed_population=population;
function decoded_value = decoding_function(population,pop_size)
% 将染色体解码为问题的实际值
decoded_value=zeros(1,pop_size); % 初始化输出向量，用于存储每个个体的适应度函数值
for i=1:pop_size
decoded_value(i) = target_fitness_function(population(i,:)); % 应用目标函数计算当前编码对应的数值结果
end
function fitness_result=target_fitness_function(current_encoding)
% 定义逼近序列[9,8,7,6,5,4,3,2,1]的目标适应度函数
fitness_result=sum(abs(target_sequence-current_encoding));
target_sequence=[9 8 7 6 5 4 3 2 1]; % 目标编码
end
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