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Task/Evolutionary-algorithm/MATLAB/evolutionary-algorithm-3.m

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+%% Genetic Algorithm -- Solves For A User Input String
+
+% #### PLEASE NOTE: you can change the selection and crossover type in the
+% parameters and see how the algorithm changes. ####
+
+clear;close all;clc;    %Clears variables, closes windows, and clears the command window
+tic                     % Begins the timer
+
+%% Select Target String
+target  = 'METHINKS IT IS LIKE A WEASEL';
+% *Can Be Any String With Any Values and Any Length!*
+% but for this example we use 'METHINKS IT IS LIKE A WEASEL'
+
+%% 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)
+S       = 4;                                    % Tournament Size (2-6 produce good results)
+best    = Inf;                                  % Initialize Best (arbitrarily large)
+MaxVal  = max(double(target));                  % Max Integer Value Needed
+ideal   = double(target);                       % Convert Target to Integers
+
+selection = 0;                                  % 0: Tournament
+                                                % 1: 50% Truncation
+
+crossover = 1;                                  % 0: Uniform crossover
+                                                % 1: 1 point crossover
+                                                % 2: 2 point crossover
+%% 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
+
+    % The fitness function starts by converting the characters into integers and then
+    % subtracting each element of each member of the population from each element of
+    % the target string. The function then takes the absolute value of
+    % the differences and sums each row and stores the function as a mx1 matrix.
+
+    F = sum(abs(bsxfun(@minus,Pop,ideal)),2);
+
+
+
+    % Finding Best Members for Score Keeping and Printing Reasons
+    [current,currentGenome] = min(F);   % current is the minimum value of the fitness array F
+                                        % currentGenome is the index of that value in the F array
+
+    % Stores New Best Values and Prints New Best Scores
+    if current < best
+        best = current;
+        bestGenome = Pop(currentGenome,:); % Uses that index to find best value
+
+        fprintf('Gen: %d  |  Fitness: %d  |  ',Gen, best);  % Formatted printing of generation and fitness
+        disp(char(bestGenome));                             % Best genome so far
+    elseif best == 0
+        break                                               % Stops program when we are done
+    end
+
+    %% Selection
+
+    % TOURNAMENT
+    if selection == 0
+    T = round(rand(2*popSize,S)*(popSize-1)+1);                     % Tournaments
+    [~,idx] = min(F(T),[],2);                                       % Index to Determine Winners
+    W = T(sub2ind(size(T),(1:2*popSize)',idx));                     % Winners
+
+    % 50% TRUNCATION
+    elseif selection == 1
+    [~,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
+    end
+
+    %% Crossover
+
+    % UNIFORM CROSSOVER
+    if crossover == 0
+    idx = logical(round(rand(size(Pop))));                          % Index of Genome from Winner 2
+    Pop2 = Pop(W(1:2:end),:);                                       % Set Pop2 = Pop Winners 1
+    P2A = Pop(W(2:2:end),:);                                        % Assemble Pop2 Winners 2
+    Pop2(idx) = P2A(idx);                                           % Combine Winners 1 and 2
+
+    % 1-POINT CROSSOVER
+    elseif crossover == 1
+    Pop2 = Pop(W(1:2:end),:);                                       % New Population From Pop 1 Winners
+    P2A = Pop(W(2:2:end),:);                                        % Assemble the New Population
+    Ref = ones(popSize,1)*(1:genome);                               % The Reference Matrix
+    idx = (round(rand(popSize,1)*(genome-1)+1)*ones(1,genome))>Ref; % Logical Indexing
+    Pop2(idx) = P2A(idx);                                           % Recombine Both Parts of Winners
+
+    % 2-POINT CROSSOVER
+    elseif crossover == 2
+    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
+    end
+    %% 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