#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 .
*/
#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;
using HeuristicLab.Selection;
namespace HeuristicLab.Optimization.Operators.LCS {
[Item("NichingTournamentSelector", "Description missing")]
[StorableClass]
public class NichingTournamentSelector : StochasticSingleObjectiveSelector, INichingSingleObjectiveSelector {
#region Parameter Properties
public ValueLookupParameter GroupSizeParameter {
get { return (ValueLookupParameter)Parameters["GroupSize"]; }
}
public ILookupParameter GAssistNichesProblemDataParameter {
get { return (LookupParameter)Parameters["GAssistNichesProblemData"]; }
}
public ILookupParameter NichingParameter {
get { return (LookupParameter)Parameters["Niching"]; }
}
public IValueLookupParameter ParentsPerChildParameter {
get { return (IValueLookupParameter)Parameters["ParentsPerChild"]; }
}
public ILookupParameter> IndividualParameter {
get { return (ILookupParameter>)Parameters["Individual"]; }
}
#endregion
[StorableConstructor]
protected NichingTournamentSelector(bool deserializing) : base(deserializing) { }
protected NichingTournamentSelector(NichingTournamentSelector original, Cloner cloner)
: base(original, cloner) {
}
public NichingTournamentSelector()
: base() {
Parameters.Add(new ValueLookupParameter("GroupSize", "The size of the tournament group.", new IntValue(2)));
Parameters.Add(new LookupParameter("GAssistNichesProblemData", ""));
Parameters.Add(new LookupParameter("Niching", ""));
Parameters.Add(new ValueLookupParameter("ParentsPerChild", ""));
Parameters.Add(new ScopeTreeLookupParameter("Individual", ""));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new NichingTournamentSelector(this, cloner);
}
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.Where(x => IsValidQuality(x.Value)).Select(x => x.Value).ToList();
List individuals = IndividualParameter.ActualValue.ToList();
int groupSize = GroupSizeParameter.ActualValue.Value;
IScope[] selected = new IScope[count];
bool doNiching = NichingParameter.ActualValue.Value;
//check if list with indexes is as long as the original scope list
//otherwise invalid quality values were filtered
if (qualities.Count != scopes.Count && individuals.Count != scopes.Count) {
throw new ArgumentException("The scopes contain invalid quality values (either infinity or double.NaN) on which the selector cannot operate.");
}
int parentsPerChild = ParentsPerChildParameter.ActualValue.Value;
var nicheComparer = GAssistNichesProblemDataParameter.ActualValue.GetPossibleNiches().First().Comparer;
var selectPerNiche = new Dictionary(nicheComparer);
var nicheScope = new Dictionary>(nicheComparer);
for (int i = 0; i < individuals.Count; i++) {
if (!nicheScope.ContainsKey(individuals[i].Niche)) {
nicheScope.Add(individuals[i].Niche, new List());
}
nicheScope[individuals[i].Niche].Add(i);
}
var possibleNiches = nicheScope.Keys.ToList();
foreach (var niche in possibleNiches) {
selectPerNiche.Add(niche, count / possibleNiches.Count);
}
int curCount = 0;
while (curCount < count) {
IGAssistNiche niche = null;
int best = -1;
if (doNiching) {
niche = GetNiche(random, selectPerNiche, possibleNiches);
} else {
best = random.Next(scopes.Count);
}
for (int i = 0; i < parentsPerChild; i++) {
int index;
if (doNiching) {
best = nicheScope[niche][random.Next(nicheScope[niche].Count)];
}
for (int j = 1; j < groupSize; j++) {
if (niche != null) {
index = nicheScope[niche][random.Next(nicheScope[niche].Count)];
} else {
index = random.Next(scopes.Count);
}
if (((maximization) && (qualities[index] > qualities[best])) ||
((!maximization) && (qualities[index] < qualities[best]))) {
best = index;
}
}
niche = individuals[best].Niche;
if (copy)
selected[curCount] = (IScope)scopes[best].Clone();
else {
selected[curCount] = scopes[best];
scopes.RemoveAt(best);
qualities.RemoveAt(best);
}
selectPerNiche[niche]--;
curCount++;
}
}
return selected;
}
private IGAssistNiche GetNiche(IRandom random, Dictionary selectPerNiche, List possibleNiches) {
int sum = selectPerNiche.Values.Sum();
if (sum <= 0) { return possibleNiches[random.Next(possibleNiches.Count)]; }
int pos = random.Next(sum);
int total = 0;
IGAssistNiche niche = selectPerNiche.Keys.First();
foreach (var item in selectPerNiche) {
total += item.Value;
niche = item.Key;
if (pos < total) {
return niche;
}
}
throw new ArgumentException("error in code");
}
}
}