二进制遗传算法（Binary Genetic Algorithm）

function out = RunGA(problem, params) % Problem CostFunction = problem.CostFunction; nVar = problem.nVar; % Params MaxIt = params.MaxIt; nPop = params.nPop; beta = params.beta; pC = params.pC; nC = round(pC*nPop/2)*2; mu = params.mu; % Template for Empty Individuals empty_individual.Position = []; empty_individual.Cost = []; % Best Solution Ever Found bestsol.Cost = inf; % Initialization pop = repmat(empty_individual, nPop, 1); for i = 1:nPop % Generate Random Solution pop(i).Position = randi([0, 1], 1, nVar); % Evaluate Solution pop(i).Cost = CostFunction(pop(i).Position); % Compare Solution to Best Solution Ever Found if pop(i).Cost < bestsol.Cost bestsol = pop(i); end end % Best Cost of Iterations bestcost = nan(MaxIt, 1); % Main Loop for it = 1:MaxIt % Selection Probabilities c = [pop.Cost]; avgc = mean(c); if avgc ~= 0 c = c/avgc; end probs = exp(-beta*c); % Initialize Offsprings Population popc = repmat(empty_individual, nC/2, 2); % Crossover for k = 1:nC/2 % Select Parents p1 = pop(RouletteWheelSelection(probs)); p2 = pop(RouletteWheelSelection(probs)); % Perform Crossover [popc(k, 1).Position, popc(k, 2).Position] = ... MyCrossover(p1.Position, p2.Position); end % Convert popc to Single-Column Matrix popc = popc(:); % Mutation for l = 1:nC % Perform Mutation popc(l).Position = Mutate(popc(l).Position, mu); % Evaluation popc(l).Cost = CostFunction(popc(l).Position); % Compare Solution to Best Solution Ever Found if popc(l).Cost < bestsol.Cost bestsol = popc(l); end end % Merge and Sort Populations pop = SortPopulation([pop; popc]); % Remove Extra Individuals pop = pop(1:nPop); % Update Best Cost of Iteration bestcost(it) = bestsol.Cost; % Display Itertion Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(bestcost(it))]); end % Results out.pop = pop; out.bestsol = bestsol; out.bestcost = bestcost; end