%
% 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('../Chapter3'); % get our continous genetic algorithm

% Parameters used in the genetic algorithm
% 
N_pop  = 300; % the population size
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 
%   4=> blending method (3.9)
%   5=> blending method (3.13) or the books suggested method
%
crossover_method = 5;

% extract points on the pareto front: 
%
x_pareto = [];
wArray = 0.1:0.1:0.9;
  
bounds = [ 1., +2 ; 1, +2 ; 1., +2 ; 1, +2 ]; % the objective functions input domain 

for ii = 1:length(wArray)
  disp( ['finding optimum of blended function  with w= ', num2str(wArray(ii)) ] );
  
  wfn = @(x) chap_5_prob_2_scaled_fn(x, wArray(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 );
  
  x_pareto = [ x_pareto; pop(1,:) ]; % extract the best solution found for each w value: 
end

Fs = chap_5_prob_2_original_fns(x_pareto); % evaluate the raw function at the optimal points (f_1(x^*),f_2(x^*)):

figure; 
ph_prob_2 = plot( Fs(:,1), Fs(:,2), '.-b', 'linewidth', 3 ); 
xlabel('objective function f_1'); ylabel('objective function f_2'); title('pareto front');