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File org.dgpf.search.algorithms.ga.selection.NPGASelection.java

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/*
 * Copyright (c) 2006 Thomas Weise
 *
 * E-Mail           : tweise@gmx.de
 * Creation Date    : 2006-05-18 06:15:46
 * Original Filename: org.dgpf.search.algorithms.ga.selection.NPGASelection.java
 * Version          : 2.0.2
 * Last modification: 2006-05-31
 *                by: Thomas Weise
 *
 * License          : GNU LESSER GENERAL PUBLIC LICENSE
 *                    Version 2.1, February 1999
 *                    You should have received a copy of this license along
 *                    with this library; if not, write to the Free Software
 *                    Foundation, Inc., 59 Temple Place, Suite 330, Boston,
 *                    MA 02111-1307, USA or download the license under
 *                    http://www.gnu.org/copyleft/lesser.html.
 *
 * Warranty         : This software is provided "as is" 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.
 */


package org.dgpf.search.algorithms.ga.selection;

import java.io.Serializable;

import org.dgpf.search.algorithms.ga.SelectionAlgorithm;
import org.dgpf.search.algorithms.ga.sorting.DominationFitnessSorting;
import org.dgpf.search.api.Individual;
import org.sfc.math.stochastic.Randomizer;

/**
 * <p>
 * The selection algorithm NPGASelection. NPGASelection has been introduced in
 * <i>J. Horn, N. Nfpliotis, and D.E. Goldberg: "A nieched pareto genetic
 * algorithm for multiobjective optimization", in Proceedings of the First
 * IEEE Conference on Evolutionary Compuation, IEEE World Congress on
 * Computational Intelligence, vol. 1, pp 82-87, Piscataway, New Jersey,
 * IEEE Service Centre, June 1994</i>.
 * Two individuals are randomly chosen and compared against a subset from
 * the entire population. If one of them is dominated (by the individuals
 * randomly chosen from the population) and the other is not, the
 * nondominated individual wins. All the other situations are considered a
 * tie (i.e., both competitors are either dominated or nondominated). Where
 * there is a tie, the result of the tournament is decided by a direct
 * comparison.</p> 
 * <p>
 * The selection parameter represents (integer) tournament size, the count
 * of individuals competing for reproduction.</p>
 *
 * @author Thomas Weise
 */

public final  class NPGASelection extends SelectionAlgorithm
  {
/**
 * The serial version uid.
 */

  private   static  final long    serialVersionUID        = 1;


/**
 * The size of the NPGASelection-tournament to perform for each reproduction
 * relative to the population size.
 */

  private final double m_size  ;

/**
 * Create a new instance of the NPGASelection selection algorithm.
 * @param p_size    The size of the NPGASelection-torunament to perform for each
 *                  reproduction relative to the population size.
 */

  public  NPGASelection  (final double  p_size)
    {
    this(p_size, false);
    }

/**
 * Create a new instance of the NPGASelection selection algorithm.
 * @param p_size    The size of the NPGASelection-torunament to perform for each
 *                  reproduction relative to the population size.
 * @param p_count_dominated This parameter is <code>true</code> if not only
 *                          the count of individuals one individual is not
 *                          dominated is used for sorting, but also the
 *                          count of individuals it is dominating itself is
 *                          used.
 */

  public  NPGASelection  (final double  p_size,
                 final boolean p_count_dominated)
    {
    super(new DominationFitnessSorting(p_count_dominated));
    this.m_size = (((p_size > 0.0d) && (p_size < 1.0d)) ? p_size : 0.1d);
    }
  
/**
 * Select one individual out of the population for reproduction (crossover
 * or mutation).
 * @param p_random        The randomizer to be used for the selection.
 * @param p_individuals   The population of individuals after being
 *                        preprocessed.
 * @param p_count         The count of valid items inside the population
 *                        array.
 * @param <Genotype>      The genotype of the individuals to select.
 * @return  An individual to reproduce.
 */

  @Override
  public  final  <Genotype extends Serializable> Individual<Genotype>
                         select(final Randomizer             p_random,
                                final Individual<Genotype>[] p_individuals,
                                final int                    p_count)
    {
          int                       l_i1, l_i2, l_g1, l_g2, l_z, l_zg, l_i;
          boolean                   l_k1, l_k2;
    final DominationFitnessSorting  l_dfs;
    
    
    l_dfs = ((DominationFitnessSorting)(this.get_sorting_algorithm()));
    l_i1 = p_random.nextInt(p_count);
    l_i2 = p_random.nextInt(p_count);
    l_g1 = l_dfs.get_fitness_group(l_i1);
    l_g2 = l_dfs.get_fitness_group(l_i2);
    l_k1 = false;
    l_k2 = false;
    
    for(l_i = (int)((0.5d + (this.m_size * p_count)));
        l_i > 0; l_i--)
      {
      l_z  = p_random.nextInt(p_count);
      l_zg = l_dfs.get_fitness_group(l_z);
      if(l_zg > l_g1)
        {
        l_k1 = true;
        if(l_k2) break;
        }
      if(l_zg > l_g2)
        {
        l_k2 = true;
        if(l_k1) break;
        }
      }
    
    if(l_k1)
      {
      if(!l_k2) return p_individuals[l_i2];
      }
    else if(l_k2) return p_individuals[l_i1];
    
    l_g1 = l_dfs.get_nieche_size(l_i1);
    l_g2 = l_dfs.get_nieche_size(l_i2);
    
    if(l_g1 < l_g2) return p_individuals[l_i1];
    if(l_g2 < l_g1) return p_individuals[l_i2];
    
    return p_individuals[((l_i1 < l_i2) ? l_i1 : l_i2)];
    }


/**
 * Returns unique, textual identifier of this genetic selection algorithm.
 * @return The unique, textual identifier of this genetic selection
 *         algorithm.
 */

  @Override
  public  final String  toString  ()
    {
    return super.toString() + '(' + this.m_size + ')';
    }
  

/**
 * Check whether this object equals another one. This method is internally
 * invoked by <code>equals()</code>. 
 * @param p_object  The object to compare with.
 * @return <code>true</code> if and only if this object equals to the other
 *         one.
 */

  @Override
  protected final boolean is_equal  (final Object p_object)
    {
    return ((p_object instanceof NPGASelection) &&
            (Double.compare(((NPGASelection)p_object).m_size,
                this.m_size)==0));
    }
  }

File Information:

file name:NPGASelection.java
package:org.dgpf.search.algorithms.ga.selection
qualified name:org.dgpf.search.algorithms.ga.selection.NPGASelection.java
file type:Java Source File
download location:download http://dgpf.sourceforge.net/source/org/dgpf/search/algorithms/ga/selection/NPGASelection.java
size:6.648 KB (6808 B)
uploaded: 2015-07-22 04:10:59 GMT+0000
last update: 2006-05-31 03:15:11 GMT+0000
last access: 2017-11-18 21:29:53 GMT+0000

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