%
% Written by:
% -- 
% John L. Weatherwax                2006-08-28
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

clear; close all; %clc; 

randn('seed',0); rand('seed',0);

addpath('../Chapter1');

% Parameters used in the genetic algorithm
% 
N_pop  = 300; % the population size
N_gene = 100; % the discreteness of the floating point representations
X_rate = 0.5; % selection rate 
mu     = 0.02; % the mutation rate
max_number_of_iterations = 20; 

% method to select mates:
%   1=> pairing from top to bottom
%   2=> random pairing
%   3=> random pairing (rank weighting)
%   4=> weighted random pairing (cost weighting)
%   5=> tournament selection 
%
parent_selection_method = 3; 

% method used to do crossover:
%   1=> single point crossover
%   2=> double point crossover 
%   3=> uniform crossover 
%
crossover_method = 1;

% a function to minimize (and its bounds)
%
funnum = 7;
bounds = [ 0, +10 ; 0, +10 ]; % the functions domain 

%funnum = 8;
%bounds = [ -10, +10 ; -10, +10 ]; % the functions domain 

%funnum = 11;
%bounds = [ -1, +1 ; -1, +1 ]; % the functions domain 

%funnum = 16;
%bounds = [ -20, +20 ; -20, +20 ]; % the functions domain 

wfn = @(x) testfunction(x,funnum); % our function handle can evaluate an entire population 
[pop,pop_costs,best_fn_values,avg_fn_values] = binary_GA( wfn, bounds, ...
         N_pop, N_gene, X_rate, mu, max_number_of_iterations, parent_selection_method, crossover_method );
disp('top 5 solution x values');
pop(1:5,:)
disp('top 5 f(x) values');
pop_costs(1:5,:)

figure; 
bh = plot( 1:length(best_fn_values), best_fn_values, '-b' ); hold on; 
ah = plot( 1:length(avg_fn_values), avg_fn_values, '-r' ); 
legend( [bh,ah], {'best f(x)','average f(x)'} );
xlabel('iteration number');

%saveas(gcf,'../../WriteUp/Graphics/Chapter2/ga_convergence.eps','epsc');