//  NSGAII_main.java
//
//  Author:
//       Antonio J. Nebro <antonio@lcc.uma.es>
//       Juan J. Durillo <durillo@lcc.uma.es>
//
//  Copyright (c) 2011 Antonio J. Nebro, Juan J. Durillo
//
//  This program is free software: you can redistribute it and/or modify
//  it under the terms of the GNU Lesser General Public License as published by
//  the Free Software Foundation, either version 3 of the License, or
//  (at your option) any later version.
//
//  This program is distributed in the hope that it will be useful,
//  but WITHOUT ANY WARRANTY; without even the implied warranty of
//  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//  GNU Lesser General Public License for more details.
//
//  You should have received a copy of the GNU Lesser General Public License
//  along with this program.  If not, see <http://www.gnu.org/licenses/>.

package jmetal.metaheuristics.nsgaII;

import jmetal.core.Algorithm;
import jmetal.core.Operator;
import jmetal.core.Problem;
import jmetal.core.SolutionSet;
import jmetal.operators.crossover.CrossoverFactory;
import jmetal.operators.mutation.MutationFactory;
import jmetal.operators.selection.SelectionFactory;
import jmetal.problems.Kursawe;
import jmetal.problems.ProblemFactory;
import jmetal.problems.ZDT.ZDT3;
import jmetal.qualityIndicator.QualityIndicator;
import jmetal.util.Configuration;
import jmetal.util.JMException;
import jmetal.util.Edge;

import java.io.IOException;
import java.security.SecureRandom;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Random;
import java.util.logging.FileHandler;
import java.util.logging.Logger;

/**
 * Class to configure and execute the NSGA-II algorithm.
 *
 * Besides the classic NSGA-II, a steady-state version (ssNSGAII) is also
 * included (See: J.J. Durillo, A.J. Nebro, F. Luna and E. Alba
 *                  "On the Effect of the Steady-State Selection Scheme in
 *                  Multi-Objective Genetic Algorithms"
 *                  5th International Conference, EMO 2009, pp: 183-197.
 *                  April 2009)
 */

public class NSGAII_main {
  public static Logger      logger_ ;      // Logger object
  public static FileHandler fileHandler_ ; // FileHandler object

  /**
   * @param args Command line arguments.
   * @throws JMException
   * @throws IOException
   * @throws SecurityException
   * Usage: three options
   *      - jmetal.metaheuristics.nsgaII.NSGAII_main
   *      - jmetal.metaheuristics.nsgaII.NSGAII_main problemName
   *      - jmetal.metaheuristics.nsgaII.NSGAII_main problemName paretoFrontFile
   */
  public static void main(String [] args) throws
                                  JMException,
                                  SecurityException,
                                  IOException,
                                  ClassNotFoundException {
    Problem   problem   ; // The problem to solve
    Algorithm algorithm ; // The algorithm to use
    Operator  crossover ; // Crossover operator
    Operator  mutation  ; // Mutation operator
    Operator  selection ; // Selection operator

    HashMap  parameters ; // Operator parameters

    QualityIndicator indicators ; // Object to get quality indicators

    // Logger object and file to store log messages
    logger_      = Configuration.logger_ ;
    fileHandler_ = new FileHandler("NSGAII_main.log");
    logger_.addHandler(fileHandler_) ;

    /* ~ Creazione del grafo ~ */

    // Creazione della lista di archi con relativo peso
    ArrayList<Edge> E = new ArrayList<Edge>();
    E.add(new Edge('A','B',7));
    E.add(new Edge('A','D',5));
    E.add(new Edge('B','D',9));
    E.add(new Edge('B','C',8));
    E.add(new Edge('C','E',5));
    E.add(new Edge('B','E',7));
    E.add(new Edge('D','E',15));
    E.add(new Edge('D','F',6));
    E.add(new Edge('E','F',8));
    E.add(new Edge('E','G',9));
    E.add(new Edge('F','G',11));

    // Creazione della lista di vertici
    ArrayList<Character> V = new ArrayList<Character>();
    V.add('A');
    V.add('B');
    V.add('C');
    V.add('D');
    V.add('E');
    V.add('F');
    V.add('G');

    //creazione di un Gene

    //{0} arco non preso
    //{1} arco considerato
    int bontà=0;
    Integer[][] array_geni = new Integer[100][E.size()];

    for(int k=0; k<100; k++) {
        int number_ones=0;
        Integer[] G = new Integer[E.size()];
        for(int i=0; i<E.size();i++) {
            G[i]=(Math.random()<0.5)?0:1;
            if(G[i] == 1) {
                number_ones++;
            }
        }
        if(number_ones==7)
            bontà++;
        array_geni[k] = G;
    }

    System.out.println("numero di geni buoni: "+bontà);



      indicators = null ;
      // Default problem
      problem = new Kursawe("Real", 3);
      //problem = new Kursawe("BinaryReal", 3);
      //problem = new Water("Real");
      //problem = new ZDT3("ArrayReal", 30);
      //problem = new ConstrEx("Real");
      //problem = new DTLZ1("Real");
      //problem = new OKA2("Real") ;
      System.out.println(problem.getName());
       // else

    algorithm = new NSGAII(problem);
    //algorithm = new ssNSGAII(problem);

    // Algorithm parameters
    algorithm.setInputParameter("populationSize",100);
    algorithm.setInputParameter("maxEvaluations",25000);

    // Mutation and Crossover for Real codification
    parameters = new HashMap() ;
    parameters.put("probability", 0.9) ;
    parameters.put("distributionIndex", 20.0) ;
    crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover", parameters);

    parameters = new HashMap() ;
    parameters.put("probability", 1.0/problem.getNumberOfVariables()) ;
    parameters.put("distributionIndex", 20.0) ;
    mutation = MutationFactory.getMutationOperator("PolynomialMutation", parameters);

    // Selection Operator
    parameters = null ;
    selection = SelectionFactory.getSelectionOperator("BinaryTournament2", parameters) ;

    // Add the operators to the algorithm
    algorithm.addOperator("crossover",crossover);
    algorithm.addOperator("mutation",mutation);
    algorithm.addOperator("selection",selection);

    // Add the indicator object to the algorithm
    algorithm.setInputParameter("indicators", indicators) ;

    // Execute the Algorithm
    long initTime = System.currentTimeMillis();
    SolutionSet population = algorithm.execute();
    long estimatedTime = System.currentTimeMillis() - initTime;

    // Result messages
    logger_.info("Total execution time: "+estimatedTime + "ms");
    logger_.info("Variables values have been writen to file VAR");
    population.printVariablesToFile("VAR");
    logger_.info("Objectives values have been writen to file FUN");
    population.printObjectivesToFile("FUN");

    if (indicators != null) {
      logger_.info("Quality indicators") ;
      logger_.info("Hypervolume: " + indicators.getHypervolume(population)) ;
      logger_.info("GD         : " + indicators.getGD(population)) ;
      logger_.info("IGD        : " + indicators.getIGD(population)) ;
      logger_.info("Spread     : " + indicators.getSpread(population)) ;
      logger_.info("Epsilon    : " + indicators.getEpsilon(population)) ;

      int evaluations = ((Integer)algorithm.getOutputParameter("evaluations")).intValue();
      logger_.info("Speed      : " + evaluations + " evaluations") ;
    } // if
  } //main
} // NSGAII_main