simpleGa.m

%% Standard Genetic Algorithm -- Solves For A User Input String
clear;close all;clc;    %Clears variables, closes windows, and clears the command window
tic                     % Begins the timer

%% Select Target String
target  = 'Hello, world!';
% *Can Be Any String With Any Values and Any Length!*


%% Parameters                    
popSize = 1000;                                 % Population Size (100-10000 generally produce good results)
genome  = length(target);                       % Genome Size
mutRate = .01;                                  % Mutation Rate (5%-25% produce good results)
best    = Inf;                                  % Initialize Best (arbitrarily large)
MaxVal  = max(double(target));                  % Max Integer Value Needed
ideal   = double(target);                       % Convert Target to Integers
%% Initialize Population
Pop = round(rand(popSize,genome)*(MaxVal-1)+1); % Creates Population With Corrected Genome Length

for Gen = 1:1e6                                 % A Very Large Number Was Chosen, But Shouldn't Be Needed
    
    %% Fitness
    F = sum(abs(bsxfun(@minus,Pop,ideal)),2);       
    if (min(F) == 0)
        [~,idx] = sort(F);
        break
    end
    %% Selection - 50% Truncation
    [~,V] = sort(F,'descend');                                      % Sort Fitness in Ascending Order
    V = V(popSize/2+1:end);                                         % Winner Pool
    W = V(round(rand(2*popSize,1)*(popSize/2-1)+1))';               % Winners    

    %% Crossover - 2 point
    Pop2 = Pop(W(1:2:end),:);                                       % New Pop is Winners of old Pop
    P2A  = Pop(W(2:2:end),:);                                       % Assemble Pop2 Winners 2
    Ref  = ones(popSize,1)*(1:genome);                              % Ones Matrix
    CP   = sort(round(rand(popSize,2)*(genome-1)+1),2);             % Crossover Points
    idx = CP(:,1)*ones(1,genome)<Ref&CP(:,2)*ones(1,genome)>Ref;    % Index
    Pop2(idx)=P2A(idx);                                             % Recombine Winners
    
    %% Mutation 
    idx = rand(size(Pop2))<mutRate;                                 % Index of Mutations
    Pop2(idx) = round(rand([1,sum(sum(idx))])*(MaxVal-1)+1);        % Mutated Value
    
    %% Reset Poplulations
    Pop = Pop2;                                                   
   
end
toc % Ends timer and prints elapsed time

disp('Best Fitness: ');
disp(min(F));
disp('Best Genome: ');
disp(char(Pop(idx(1),:)));