【ANN预测】基于遗传算法优化 ANN附matlab代码.zip

function[BestRepGA,BestRepGAPOS]=GA(nPop,MaxIt,lb,up,D,func) %% Problem Definition VarSize=[1 D]; % Decision Variables Matrix Size %% Type of selection methods ANSWER=questdlg('Choose Selection Method:','Real Genetic Algorithm',... 'Roulette Wheel Selection','Tournament Selection','Random Selection','Roulette Wheel Selection'); UseRouletteWheelSelection=strcmp(ANSWER,'Roulette Wheel Selection'); UseTournamentSelection=strcmp(ANSWER,'Tournament Selection'); UseRandomSelection=strcmp(ANSWER,'Random Selection'); if UseRouletteWheelSelection beta=8; % Selection Pressure elseif UseTournamentSelection beta=3; % Tournamnet Pressure elseif UseRandomSelection beta=5; end if UseTournamentSelection TournamentSize=3; % Tournamnet Size end %% Parameters prompt = {' Enter Crossover Percentage(0.1< nc <0.8)','Enter Mutation Percentage (1 - nc)'}; dlgtitle = 'GA Parameters'; dims = [1 50]; definput = {'0.8','0.2'}; answer = inputdlg(prompt,dlgtitle,dims,definput); answer=str2double(answer); pc=answer(1); % Crossover Percentage nc=2*round(pc*nPop/2); % Number of Offsprings (Parnets) pm=answer(2); % Mutation Percentage nm=round(pm*nPop); % Number of Mutants gamma=0.05; mu=0.02; % Mutation Rate\ pause(0.1); %% Initialization empty_individual.Position=[]; empty_individual.Cost=[]; pop=repmat(empty_individual,nPop,1); for i=1:nPop % Initialize Position pop(i).Position=unifrnd(lb,up,VarSize); % Evaluation pop(i).Cost=func(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; % Array to Hold Number of Function Evaluations nfe=zeros(MaxIt,1); %% Main Loop for it=1:MaxIt % Calculate Selection Probabilities 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 if UseRouletteWheelSelection i1=RouletteWheelSelection(P); i2=RouletteWheelSelection(P); end if UseTournamentSelection i1=TournamentSelection(pop,TournamentSize); i2=TournamentSelection(pop,TournamentSize); end if UseRandomSelection i1=randi([1 nPop]); i2=randi([1 nPop]); end % Select Parents p1=pop(i1); p2=pop(i2); % Apply Crossover [popc(k,1).Position, popc(k,2).Position]=... Crossover(p1.Position,p2.Position,gamma,lb,up); % Evaluate Offsprings popc(k,1).Cost=feval(func,popc(k,1).Position); popc(k,2).Cost=feval(func,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,lb,up); % Evaluate Mutant popm(k).Cost=feval(func,popm(k).Position); end % Create Merged Population pop=[pop popc popm]; % 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; BestPos(it,:)=BestSol.Position; % Show Iteration Information disp(['Iteration ' num2str(it) , 'Best Cost = ' num2str(BestCost(it))]); end BestRepGA=BestCost; BestRepGAPOS=BestPos(MaxIt,:); %% Results figure; semilogy(BestCost,'r','LineWidth',2); legend('Real Genetic Algorithm'); title(ANSWER) xlabel('Number of Iterations '); ylabel('RMSE'); grid on set(gca,'fontname','Times new roman','FontSize', 10); % Set fontname end