source: branches/HeuristicLab.EvolutionTracking/HeuristicLab.EvolutionTracking/3.4/Operators/BeforeCrossoverOperator.cs @ 10890

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.EvolutionTracking {
  [StorableClass]
  [Item("AfterCrossoverOperator", "A generic operator that can record genealogical relationships between crossover parents and children.")]
  public class BeforeCrossoverOperator<T> : EvolutionTrackingOperator<T>, ICrossoverOperator<T> where T : class,IItem {
    private const string ParentsParameterName = "Parents";
    private const string ChildParameterName = "Child";

    public IScopeTreeLookupParameter<T> ParentsParameter {
      get { return (IScopeTreeLookupParameter<T>)Parameters[ParentsParameterName]; }
    }
    public ILookupParameter<T> ChildParameter {
      get { return (ILookupParameter<T>)Parameters[ChildParameterName]; }
    }

    public IValueParameter<IntValue> ExecutionCountParameter {
      get { return (IValueParameter<IntValue>)Parameters["ExecutionCount"]; }
    }

    protected BeforeCrossoverOperator(BeforeCrossoverOperator<T> original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new BeforeCrossoverOperator<T>(this, cloner);
    }

    public BeforeCrossoverOperator() {
      Parameters.Add(new ScopeTreeLookupParameter<T>(ParentsParameterName));
      Parameters.Add(new LookupParameter<T>(ChildParameterName));
      Parameters.Add(new ValueParameter<IntValue>("ExecutionCount", new IntValue(0)));
    }

    protected List<IGenealogyGraphNode<T>> CurrentGeneration {
      get {
        if (GenealogyGraph.Ranks.ContainsKey(Generations.Value))
          return GenealogyGraph.Ranks[Generations.Value].Cast<IGenealogyGraphNode<T>>().ToList();
        return null;
      }
    }

    private readonly Func<IScope, string> getScopeId = s => ((StringValue)s.Variables["Id"].Value).Value;
    public override IOperation Apply() {
      ExecutionCountParameter.Value.Value++;

      var subScopes = ExecutionContext.Scope.SubScopes;
      var parentVertices = subScopes.Select(x => (GenealogyGraphNode<T>)GenealogyGraph.GetVertex(getScopeId(x))).ToList();

      var parents = ParentsParameter.ActualValue.ToList();

      var childVertex = new GenealogyGraphNode<T> {
        // the child parameter does not have a value yet (it will be assigned after crossover), 
        // but the first parent is actually the future child so we use this
        Content = parents[0],
        Rank = parentVertices[0].Rank + 1
      };

      GenealogyGraph.AddVertex(childVertex);

      foreach (var parentVertex in parentVertices) {
        GenealogyGraph.AddArc(parentVertex, childVertex);
      }
      ExecutionContext.Scope.Variables.Add(new Variable("Id", new StringValue(childVertex.Id)));

      return base.Apply();
    }
  }
}