#region License Information
/* HeuristicLab
* Copyright (C) 2002-2008 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.Text;
using HeuristicLab.Core;
using HeuristicLab.Data;
namespace HeuristicLab.Selection.OffspringSelection {
public class OffspringAnalyzer : OperatorBase {
public override string Description {
get { return @"TODO\r\nOperator description still missing ..."; }
}
public OffspringAnalyzer() {
AddVariableInfo(new VariableInfo("Maximization", "Problem is a maximization problem", typeof(BoolData), VariableKind.In));
AddVariableInfo(new VariableInfo("Quality", "Quality value", typeof(DoubleData), VariableKind.In));
AddVariableInfo(new VariableInfo("ParentQualities", "Temporarily store parent qualities", typeof(DoubleArrayData), VariableKind.New | VariableKind.Out | VariableKind.In | VariableKind.Deleted));
AddVariableInfo(new VariableInfo("SuccessfulChild", "True if the child was successful", typeof(BoolData), VariableKind.New));
VariableInfo compFactorInfo = new VariableInfo("ComparisonFactor", "Factor for comparing the quality of a child with the qualities of its parents (0 = better than worst parent, 1 = better than best parent)", typeof(DoubleData), VariableKind.In);
compFactorInfo.Local = true;
AddVariableInfo(compFactorInfo);
AddVariable(new Variable("ComparisonFactor", new DoubleData(0.5)));
VariableInfo parentCount = new VariableInfo("ParentsCount", "How many parents created the offspring", typeof(IntData), VariableKind.In);
parentCount.Local = true;
AddVariableInfo(parentCount);
AddVariable(new Variable("ParentsCount", new IntData(2)));
}
public override IOperation Apply(IScope scope) {
bool maximize = GetVariableValue("Maximization", scope, true).Data;
double compFact = GetVariableValue("ComparisonFactor", scope, true).Data;
int parentsCount = GetVariableValue("ParentsCount", scope, true).Data;
if (parentsCount < 1) throw new InvalidOperationException("OffspringAnalyzer: ParentsCount must be >= 1");
DoubleArrayData qualities = GetVariableValue("ParentQualities", scope, false, false);
if (qualities == null) {
// fetch and store parent qualities
IVariableInfo qualityInfo = GetVariableInfo("Quality");
double[] qualitiesArray = new double[scope.SubScopes.Count];
for (int i = 0; i < qualitiesArray.Length; i++)
qualitiesArray[i] = scope.SubScopes[i].GetVariableValue(qualityInfo.FormalName, false).Data;
qualities = new DoubleArrayData(qualitiesArray);
IVariableInfo parentQualitiesInfo = GetVariableInfo("ParentQualities");
if (parentQualitiesInfo.Local)
AddVariable(new Variable(parentQualitiesInfo.ActualName, qualities));
else
scope.AddVariable(new Variable(scope.TranslateName(parentQualitiesInfo.FormalName), qualities));
CompositeOperation next = new CompositeOperation();
next.AddOperation(new AtomicOperation(SubOperators[0], scope));
next.AddOperation(new AtomicOperation(this, scope));
return next;
} else {
int crossoverEvents = qualities.Data.Length / parentsCount;
int childrenPerCrossoverEvent = scope.SubScopes.Count / crossoverEvents;
if (scope.SubScopes.Count % crossoverEvents != 0 ||
childrenPerCrossoverEvent < 1) throw new InvalidOperationException("OffspringAnalyzer: The number of children per crossover event has to be constant and >= 1");
// analyze offspring of all crossoverEvents
for (int i = 0; i < crossoverEvents; i++) {
double worstParent = double.MaxValue; // lowest quality parent
double bestParent = double.MinValue; // highest quality parent
for (int y = 0; y < parentsCount; y++) {
if (qualities.Data[i * parentsCount + y] < worstParent) worstParent = qualities.Data[i * parentsCount + y];
if (qualities.Data[i * parentsCount + y] > bestParent) bestParent = qualities.Data[i * parentsCount + y];
}
for (int j = 0; j < childrenPerCrossoverEvent; j++) {
IVariableInfo qualityInfo = GetVariableInfo("Quality");
double child = scope.SubScopes[i * childrenPerCrossoverEvent + j].GetVariableValue(qualityInfo.FormalName, false).Data;
double threshold;
if (!maximize)
threshold = bestParent + (worstParent - bestParent) * compFact;
else
threshold = worstParent + (bestParent - worstParent) * compFact;
IVariableInfo successfulInfo = GetVariableInfo("SuccessfulChild");
BoolData successful;
if (((!maximize) && (child < threshold)) ||
((maximize) && (child > threshold)))
successful = new BoolData(true);
else
successful = new BoolData(false);
scope.SubScopes[i * childrenPerCrossoverEvent + j].AddVariable(new Variable(scope.TranslateName(successfulInfo.FormalName), successful));
}
}
// remove parent qualities again
IVariableInfo parentQualitiesInfo = GetVariableInfo("ParentQualities");
if (parentQualitiesInfo.Local)
RemoveVariable(parentQualitiesInfo.ActualName);
else
scope.RemoveVariable(scope.TranslateName(parentQualitiesInfo.FormalName));
return null;
}
}
}
}