使用GA的二进制特征选择

% % Copyright (c) 2015, Yarpiz (www.yarpiz.com) % All rights reserved. Please read the "license.txt" for license terms. % % Project Code: YPML122 % Project Title: Feature Selection using GA (Variable Number of Features) % Publisher: Yarpiz (www.yarpiz.com) % % Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team) % % Contact Info: sm.kalami@gmail.com, info@yarpiz.com % clc; clear; close all; %% Problem Definition data=LoadData(); CostFunction=@(s) FeatureSelectionCost(s,data); % Cost Function nVar=data.nx; % Number of Decision Variables VarSize=[1 nVar]; % Decision Variables Matrix Size %% GA Parameters MaxIt=10; % Maximum Number of Iterations nPop=10; % Population Size pc=0.7; % Crossover Percentage nc=2*round(pc*nPop/2); % Number of Offsprings (Parnets) pm=0.3; % Mutation Percentage nm=round(pm*nPop); % Number of Mutants mu=0.1; % Mutation Rate beta=8; % Selection Pressure %% Initialization disp('Initialization ...'); empty_individual.Position=[]; empty_individual.Cost=[]; empty_individual.Out=[]; pop=repmat(empty_individual,nPop,1); for i=1:nPop % Initialize Position pop(i).Position=randi([0 1],VarSize); % Evaluation [pop(i).Cost, pop(i).Out]=CostFunction(pop(i).Position); end % Sort Population Costs=[pop.Cost]; [Costs, SortOrder]=sort(Costs); pop=pop(SortOrder); % Store Best Solution BestSol=pop(1); % Array to Hold Best Cost Values BestCost=zeros(MaxIt,1); % Store Cost WorstCost=pop(end).Cost; %% Main Loop for it=1:MaxIt disp(['Starting Iteration ' num2str(it) ' ...']); P=exp(-beta*Costs/WorstCost); P=P/sum(P); % Crossover popc=repmat(empty_individual,nc/2,2); for k=1:nc/2 % Select Parents Indices i1=RouletteWheelSelection(P); i2=RouletteWheelSelection(P); % Select Parents p1=pop(i1); p2=pop(i2); % Apply Crossover [popc(k,1).Position, popc(k,2).Position]=Crossover(p1.Position,p2.Position); % Evaluate Offsprings [popc(k,1).Cost, popc(k,1).Out]=CostFunction(popc(k,1).Position); [popc(k,2).Cost, popc(k,2).Out]=CostFunction(popc(k,2).Position); end popc=popc(:); % Mutation popm=repmat(empty_individual,nm,1); for k=1:nm % Select Parent i=randi([1 nPop]); p=pop(i); % Apply Mutation popm(k).Position=Mutate(p.Position,mu); % Evaluate Mutant [popm(k).Cost, popm(k).Out]=CostFunction(popm(k).Position); end % Create Merged Population pop=[pop popc popm]; %#ok % Sort Population Costs=[pop.Cost]; [Costs, SortOrder]=sort(Costs); pop=pop(SortOrder); % Update Worst Cost WorstCost=max(WorstCost,pop(end).Cost); % Truncation pop=pop(1:nPop); Costs=Costs(1:nPop); % Store Best Solution Ever Found BestSol=pop(1); % Store Best Cost Ever Found BestCost(it)=BestSol.Cost; % Show Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); end %% Results figure; plot(BestCost,'LineWidth',2); ylabel('Cost');