gab1.m

clc;
clear;
close all;

%% Problem Definition

CostFunction=@(x) MinOne(x);     % Fitness Function

nVar=30;            % Number of Decision Variables

VarSize=[1 nVar];   % Decision Variables Matrix Size

%% GA Parameters

MaxIt=100;	% Maximum Number of Iterations

nPop=40;	% Population Size


pc=0.6;                 % Crossover Percentage
nc=2*round(pc*nPop/2);  % Number of Offsprings (also Parnets)

pm=0.04;                 % Mutation Percentage
nm=round(pm*nPop);      % Number of Mutants
mu=0.02;                % Mutation Rate

ANSWER=questdlg('Choose selection method:','Genetic Algorith',...
    'Roulette Wheel','Random','Roulette Wheel');

UseRouletteWheelSelection=strcmp(ANSWER,'Roulette Wheel');
UseRandomSelection=strcmp(ANSWER,'Random');

if UseRouletteWheelSelection
    beta=80;         % Selection Pressure
end



pause(0.01); % Needed due to a bug in older versions of MATLAB

%% Initialization

empty_individual.Position=[];
empty_individual.Cost=[];

pop=repmat(empty_individual,nPop,1);

for i=1:nPop
    
    % Initialize Position
    pop(i).Position=randi([0 1],VarSize);
    
    % Evaluation
    pop(i).Cost=CostFunction(pop(i).Position);
    
end

% Sort Population
Costs=[pop.Cost];
[Costs, SortOrder]=sort(Costs,'descend');
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
    
    % Calculate Selection Probabilities
    if UseRouletteWheelSelection
        P=exp(-beta*Costs/WorstCost);
        P=P/sum(P);
    end
    
    % 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 UseRandomSelection
            i1=randi([1 nPop]);
            i2=randi([1 nPop]);
        end

        % Select Parents
        p1=pop(i1);
        p2=pop(i2);
        
        % Perform Crossover
        [popc(k,1).Position, popc(k,2).Position]=Crossover(p1.Position,p2.Position);
        
        % Evaluate Offsprings
        popc(k,1).Cost=CostFunction(popc(k,1).Position);
        popc(k,2).Cost=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);
        
        % Perform Mutation
        popm(k).Position=Mutate(p.Position,mu);
        
        % Evaluate Mutant
        popm(k).Cost=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 Min = ' num2str(BestCost(it))]);
    
end

%% Results

figure;
plot(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Minimun');
grid on;