遗传算法matlab代码实现

function [threshold, thresholds, t, max_fitness, max_fitnesses] = get_threshold(image, N, Pc, Pm, epsilon, stop) % 利用遗传算法计算分割阈值 % image 输入灰度图像; N 种群规模; eplison 最大换代数; % Pc 交叉率; Pm 变异率; stop 最大适应度停止次数 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 设置默认值 scale = 255; % 灰度值范围[0,255] L = 256; % 索引最大值256，和灰度值范围相差1 t = 1; % 记录迭代次数 S = zeros(1,N); % 根据种群规模构建种群，默认值灰度值是0 t_stop = 0; % 种群连续多次未进化停止算法 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 灰度直方图分布信息 [p, u] = image_hist(image); %随机产生N个0到255之间的整数作为灰度值，构建种群S for i=1:N S(i) = randi(L)-1; % 产生0到255的随机整数 end %计算适应度 fitnesses = get_fitness(S, p, u, scale, L); max_fitness = max(fitnesses); max_fitnesses(t) = max_fitness; for i=1:size(fitnesses,2) if fitnesses(i) == max_fitness break; end end thresholds(t) = S(i); %开始迭代 %迭代条件 1.总迭代次数不到上上限。 2.停止次数没到阈值。 while t <= epsilon && t_stop <= stop %概率选择 indecs = probility_seletion(N, fitnesses); %产生新的种群 S1 = S(indecs); %交叉 crossover(); %变异 mutation(); %此时的S1就是变异后的种群 %计算适应度 new_fitness = get_fitness(S1, p, u, scale,L); new_max_fitness = max(new_fitness); if new_max_fitness > max_fitness %进化成功 S = S1; max_fitness = new_max_fitness; fitnesses = new_fitness; t_stop = 1; else t_stop = t_stop+1; end t = t+1; max_fitnesses(t) = max_fitness; for i=1:size(fitnesses,2) if fitnesses(i) == max_fitness break; end end thresholds(t) = S(i); end threshold = thresholds(t); %交叉 对S1进行交叉 function crossover() %需要参与交换的染色体数目 c_count = int8(N * Pc); %交换前cross_length位 cross_length = size(dec2bin(S1(1)),2)/2; for i = 1:2:c_count if i+1 <= c_count bin1 = dec2bin(S1(i),8); bin2 = dec2bin(S1(i+1),8); top_bin1 = bin1(1:int8(cross_length)); top_bin2 = bin2(1:int8(cross_length)); bin1(1:int8(cross_length)) = top_bin2; bin2(1:int8(cross_length)) = top_bin1; S1(i) = bin2dec(bin1); S1(i+1) = bin2dec(bin2); end end end %变异 对S1进行变异 function mutation() for i=1:N rand_n = rand; if(rand_n <= Pm) %变异 决定变异位置 bin_m = dec2bin(S1(i)); index_m = randi(size(bin_m,2)); if(bin_m(index_m) == '1') bin_m(index_m) = '0'; else bin_m(index_m) = '1'; end S1(i) = bin2dec(bin_m); end end end end