source: branches/LearningClassifierSystems/HeuristicLab.Encodings.DecisionList/3.3/Crossover/SinglePointCrossover.cs @ 9334

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab 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 General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Encodings.DecisionList.Crossover {
  [Item("SinglePointCrossover", "Description missing")]
  [StorableClass]
  public class SinglePointCrossover : DecisionListCrossover {

    #region Parameter Properties
    #endregion

    [StorableConstructor]
    protected SinglePointCrossover(bool deserializing) : base(deserializing) { }
    protected SinglePointCrossover(SinglePointCrossover original, Cloner cloner)
      : base(original, cloner) {
    }
    public SinglePointCrossover()
      : base() {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SinglePointCrossover(this, cloner);
    }

    public static DecisionList Apply(IRandom random, DecisionList parent1, DecisionList parent2) {
      int rulesP1 = random.Next(0, parent1.Rules.Count());
      int rulesP2 = random.Next(0, parent2.Rules.Count());

      var rules = new List<Rule>(rulesP1 + rulesP2);
      for (int i = 0; i < rulesP1; i++) {
        rules.Add((Rule)parent1.Rules[i].Clone());
      }

      rules.Add(parent1.Rules[rulesP1].Crossover(parent2.Rules[rulesP2], random));

      for (int i = rulesP2 + 1; i < parent2.Rules.Count(); i++) {
        rules.Add((Rule)parent2.Rules[i].Clone());
      }

      return new DecisionList(rules);
    }

    protected override DecisionList Cross(IRandom random, ItemArray<DecisionList> parents) {
      if (parents.Length != 2) throw new ArgumentException("ERROR in SinglePointCrossover: The number of parents is not equal to 2");
      return Apply(random, parents[0], parents[1]);
    }
  }
}