#region License Information
/* HeuristicLab
* Copyright (C) 2002-2011 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 .
*/
#endregion
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Selection {
///
/// A tournament selection operator which considers a single double quality value for selection.
///
[Item("TournamentSelector", "A tournament selection operator which considers a single double quality value for selection.")]
[StorableClass]
public sealed class TournamentSelector : StochasticSingleObjectiveSelector, ISingleObjectiveSelector {
public ValueLookupParameter GroupSizeParameter {
get { return (ValueLookupParameter)Parameters["GroupSize"]; }
}
[StorableConstructor]
private TournamentSelector(bool deserializing) : base(deserializing) { }
private TournamentSelector(TournamentSelector original, Cloner cloner) : base(original, cloner) { }
public override IDeepCloneable Clone(Cloner cloner) {
return new TournamentSelector(this, cloner);
}
public TournamentSelector()
: base() {
Parameters.Add(new ValueLookupParameter("GroupSize", "The size of the tournament group.", new IntValue(2)));
}
protected override IScope[] Select(List scopes) {
int count = NumberOfSelectedSubScopesParameter.ActualValue.Value;
bool copy = CopySelectedParameter.Value.Value;
IRandom random = RandomParameter.ActualValue;
bool maximization = MaximizationParameter.ActualValue.Value;
List qualities = QualityParameter.ActualValue.Select(x => x.Value).ToList();
int groupSize = GroupSizeParameter.ActualValue.Value;
IScope[] selected = new IScope[count];
for (int i = 0; i < count; i++) {
int best = random.Next(scopes.Count);
int index;
for (int j = 1; j < groupSize; j++) {
index = random.Next(scopes.Count);
if (((maximization) && (qualities[index] > qualities[best])) ||
((!maximization) && (qualities[index] < qualities[best]))) {
best = index;
}
}
if (copy)
selected[i] = (IScope)scopes[best].Clone();
else {
selected[i] = scopes[best];
scopes.RemoveAt(best);
qualities.RemoveAt(best);
}
}
return selected;
}
}
}