genetic_algorithm.m

%% This is the AiAI worksheet 3 task - Genetic Algorithm - Travelling Salesman Problem
%   Raphael Nagel
%   06/Nov/2012
clc
clear all

%% VARIABLES !!!!!EDIT HERE!!!!!
popSize=5;
geneLength=5;
generations=50;
maxCycles = 5; %how often to redo different the evolution from initiation

mutationRate=1/geneLength; % we should just about have one mutation per genom
coRate = 1;                   % how often we cross over
tournament_size = 10;
%

twoPointCO = 0;


%% set up thearray, etc.

%struct to hold the individuals  %population=struct('g',{[]},'f',[]); backup in case the other stuff doesntwork

population(1:popSize)=struct('g',{[zeros(1,geneLength)]},'f',[0]);
offspring(1:popSize)=struct('g',{[zeros(1,geneLength)]},'f',[0]);
parents(1:popSize)=struct('g',{[zeros(1,geneLength)]},'f',[0]);
rescued=struct('g',{[]},'f',[]); %will just hold the single best individual from population

% set up output for over time plotting
output=struct('bestFit',zeros(generations,maxCycles),'averageFit',zeros(generations,maxCycles),'avgBest5',zeros(generations,1),'avgAvg5',zeros(generations,1)); %preload the output file for all X cycles with nulls %per evolution

%generate a TSP distance chart  
distanceChartTSP = generate_TSP_problem(popSize, 1, 1500);


%% redo the whole GA to show how parameters influence the outcome

for evoCycles = 1:maxCycles
    
    
    %% initiate the GA with random genes
    %population = generate_binary_gene_population(population,popSize,geneLength);
    population = generate_TS_population(population,popSize,geneLength); %checked and works

    
    
    
    %% !!!!!! START THE EVOLUTION !!!!!
    for genCycles = 1:generations
        
        
        %% calculate individual fitness by summing each genes with 1 per individual
        %population = n1s_fitness(population,popSize,geneLength);
        
        %% calculate individual fitness based on paired genes (gene(n) == gene(n+1))
        %population = pair1s_fitness(population,popSize,geneLength);
        
        %% calculate individual fitness based on paired genes
       % population = strict_pairs1s_fitness(population,popSize,geneLength);
       
        %% calculate individual fitness for a TSP problem
        population = tsp_fitness(population, popSize, geneLength, distanceChartTSP); 
        
        %% calculate overall fitness,find and save best individual - it assumes a bigger number is a better fitness.
        
        totPopFitness=0;
        maxFitness = 0;
        for x = 1:popSize
            totPopFitness = (totPopFitness + population(x).f);
            if population(x).f > maxFitness
                maxFitness = population(x).f;
                rescued(1) = population(x); %save the single surviver
            end
        end
        
        
        %% also save in array
        
        output.bestFit(genCycles,evoCycles) = maxFitness;
        output.averageFit(genCycles,evoCycles) = (totPopFitness / popSize);
        
        
        %% find the parents
        %Begin summing the fitnesses
        %of the individuals in the population again until the running sum is
        % greater than or equal to the randomly chosen number.
        
        %  parents = roulette_wheel_selection(population,popSize, totPopFitness);
        parents = tournament_selection(population,popSize,tournament_size);
        
        %% Recombination - one point cross over
        % offspring = one_point_CO(parents,popSize,geneLength);
        
        %% Recombination - two point cross over
        %offspring = two_point_CO(parents,popSize,geneLength,coRate);
        
        %% Recombination - uniform cross over
        %offspring = uniform_CO(parents,popSize,geneLength,coRate);
        
        
        
        
        %% TSP crossover TBD!!!!!!!!!!!!!
        offspring = order1_TSP_CO(parents, popSize, geneLength);
        %% Mutation
       % standart binary mutation 
       %offspring = binary_mutation(offspring, popSize, geneLength, mutationRate);
       
       % swapTSP mutation
       offspring = swapTSP_Mutation(offspring, popSize, geneLength);
        
        %% Survivor Selection
        
        
        population = offspring; % first overwrite the whole population with offspring
        y = randi([1,popSize]);  %select random position to overwrite with the rescued surviver
        population(y)= rescued(1); %reinsert this fittest individual of the old population
        
        
        
    end %ends the evolution
    
end
output.avgBest5 = mean(output.bestFit,2);
output.avgAvg5 = mean(output.averageFit,2);

hold on
grid on
plot(output.bestFit,'DisplayName','output.bestFit','YDataSource','output.bestFit');figure(gcf)
plot(output.averageFit,'DisplayName','output.averageFit','YDataSource','output.averageFit');figure(gcf)
plot(output.avgBest5,'r','LineWidth',3,'DisplayName','output.avgBest5','YDataSource','output.avgBest5');figure(gcf)
plot(output.avgAvg5,'b','LineWidth',3,'DisplayName','output.avgAvg5','YDataSource','output.avgAvg5');figure(gcf)
xlabel('Generations');ylabel('Fitness');