[数学]遗传算法.ppt

MATLAB遗传算法应用举例 MATLAB 8.0 GA工具箱界面 GA的流程图 建立和保存simple_fitness.m文件 function y = simple_fitness(x) y = 100 * (x(1)^2 - x(2)) ^2 + (1 - x(1))^2; ObjectiveFunction = @simple_fitness; nvars = 2; % Number of variables LB = [0 0]; % Lower bound UB = [1 13]; % Upper bound ConstraintFunction = @simple_constraint; [x,fval] = ga(ObjectiveFunction,nvars,[],[],[],[],LB,UB, ... ConstraintFunction) options = gaoptimset(MutationFcn,@mutationadaptfeasible); % Next we run the GA solver. [x,fval] = ga(ObjectiveFunction,nvars,[],[],[],[],LB,UB, ... ConstraintFunction,options) 提供可视图 options = gaoptimset(options,PlotFcns,{@gaplotbestf,@gaplotmaxconstr}, ... Display,iter); % Next we run the GA solver. [x,fval] = ga(ObjectiveFunction,nvars,[],[],[],[],LB,UB, ... ConstraintFunction,options) 提供初始点 X0 = [0.5 0.5]; % Start point (row vector) options = gaoptimset(options,InitialPopulation,X0); % Next we run the GA solver. [x,fval] = ga(ObjectiveFunction,nvars,[],[],[],[],LB,UB, ... ConstraintFunction,options) 遗传算法和传统优化算法比较 不同于从一个点开始有哪些信誉好的足球投注网站最优解的传统的最优化算法，遗传算法从一个种群开始对问题的最优解进行并行有哪些信誉好的足球投注网站，所以更利于全局最优化解的有哪些信誉好的足球投注网站。 遗传算法并不依赖于导数信息或其他辅助信息来进行最优解有哪些信誉好的足球投注网站。 遗传算法采用的是概率型规则而不是确定性规则，所以每次得出的结果不一定完全相同，有时甚至会有较大的差异。 【例】求最小值 function f=mga(x) f=(x(1)+x(2))^2+5*(x(3)-x(4))^2+(x(2) - 2*x(3))^4+10*(x(1)-x(4))^4 x=ga(@mga,4) examp.m Optimization terminated: average change in the fitness value less than options.TolFun. x = 0.0531 0.0734 0.2078 0.1747 ff=gaoptimset; ff.Generations=2000; ff.PopulationSize=80; ff.CrossoverFcn=@crossoverheuristic; x=ga(@mga,4,ff) Optimization terminated: average change in the fitness value less than options.TolFun. x = 0.0116 -0.0118 -0.0031 -0.0031 f=inline... % 目标函数描述 ((x(1)+x(2))^2+5*(x(3)-x(4))^2+(x(2)-2*x(3))^4+10*(x(1)-x(4))^4,x) ff=optimset; ff.MaxIter=10000; ff.TolX=1e-7; tic, x=fminsearch(f,10*ones(4,1),ff); toc; x f = Inline function: f(x) = (x(1)+x(2))^2+5*(x(3)-x(4))^2+(x(2)-2*x(3))^4