%
% 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; 

addpath('../Chapter1');
addpath('../Chapter3');

% Parameters used in the genetic algorithm
% 
N_pop  = 16; % the population size
X_rate = 0.5; % selection rate 
mu     = 0.2; % 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 
%   4=> blending method (3.9)
%   5=> blending method (3.13) or the books suggested method
%
crossover_method = 5;

% 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 

N_mc = 100;
study_avg = 0; % look at average or best 

% The original (non hybrid method): 
% 
fn_values = zeros(N_mc,max_number_of_iterations);
for ii = 1:N_mc
  randn('seed',ii); rand('seed',ii);

  [pop,pop_costs,best_fn_values,avg_fn_values] = continuous_GA( wfn, bounds, ...
                                                  N_pop, X_rate, mu, max_number_of_iterations, parent_selection_method, crossover_method, 0 );

  if( study_avg==1 )
    fn_values(ii,:) = avg_fn_values;    
  else
    fn_values(ii,:) = best_fn_values;    
  end
    
end

m_fn_values_NH = mean( fn_values, 1 ); % NH = not hybrid
s_fn_values_NH = std( fn_values, 1 );

ph_nh = plot( m_fn_values_NH, '-o' ); hold on;
plot( m_fn_values_NH+2*s_fn_values_NH, '-' );
plot( m_fn_values_NH-2*s_fn_values_NH, '-' );


% The HYBRID genetic algorithm:
% 
fn_values = zeros(N_mc,max_number_of_iterations);
for ii = 1:N_mc
  randn('seed',ii); rand('seed',ii);

  [pop,pop_costs,best_fn_values,avg_fn_values] = continuous_GA( wfn, bounds, ...
                                                  N_pop, X_rate, mu, max_number_of_iterations, parent_selection_method, crossover_method, 1 );

  if( study_avg==1 )
    fn_values(ii,:) = avg_fn_values;    
  else
    fn_values(ii,:) = best_fn_values;    
  end
end

m_fn_values_H = mean( fn_values, 1 ); % H = hybrid
s_fn_values_H = std( fn_values, 1 );

ph_h = plot( m_fn_values_H, '-ko' ); 
plot( m_fn_values_H+2*s_fn_values_H, '-k' );
plot( m_fn_values_H-2*s_fn_values_H, '-k' );

legend( [ph_nh,ph_h], {'nonhybrid GA','hybrid GA'} );
xlabel('iteration number'); ylabel('population average cost');

%saveas( gcf, '../../WriteUp/Graphics/Chapter5/hybrid_F8_convergence.eps', 'epsc' );