#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 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.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Collections;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;
namespace HeuristicLab.Problems.VehicleRouting.Encodings.General {
[Item("BiasedMultiVRPSolutionManipulator", "Randomly selects and applies one of its crossovers every time it is called based on the success progress.")]
[StorableClass]
public class BiasedMultiVRPSolutionManipulator : MultiVRPSolutionManipulator {
public ValueLookupParameter ActualProbabilitiesParameter {
get { return (ValueLookupParameter)Parameters["ActualProbabilities"]; }
}
public ValueLookupParameter SuccessProgressAnalyisis {
get { return (ValueLookupParameter)Parameters["SuccessProgressAnalysis"]; }
}
public ValueLookupParameter Factor {
get { return (ValueLookupParameter)Parameters["Factor"]; }
}
public ValueParameter LowerBoundParameter {
get { return (ValueParameter)Parameters["LowerBound"]; }
}
public ValueParameter DepthParameter {
get { return (ValueParameter)Parameters["Depth"]; }
}
[StorableConstructor]
protected BiasedMultiVRPSolutionManipulator(bool deserializing) : base(deserializing) { }
protected BiasedMultiVRPSolutionManipulator(BiasedMultiVRPSolutionManipulator original, Cloner cloner) : base(original, cloner) { }
public BiasedMultiVRPSolutionManipulator()
: base() {
Parameters.Add(new ValueLookupParameter("ActualProbabilities", "The array of relative probabilities for each operator."));
Parameters.Add(new ValueLookupParameter("SuccessProgressAnalysis", "The success progress analyisis to be considered",
new StringValue("ExecutedMutationOperator")));
Parameters.Add(new ValueLookupParameter("Factor", "The factor with which the probabilities should be updated", new DoubleValue(0.2)));
Parameters.Add(new ValueParameter("LowerBound", "The depth of the individuals in the scope tree.", new DoubleValue(0.01)));
Parameters.Add(new ValueParameter("Depth", "The depth of the individuals in the scope tree.", new IntValue(1)));
SelectedOperatorParameter.ActualName = "SelectedManipulationOperator";
}
public override IDeepCloneable Clone(Cloner cloner) {
return new BiasedMultiVRPSolutionManipulator(this, cloner);
}
public override void InitializeState() {
base.InitializeState();
ActualProbabilitiesParameter.Value = null;
}
public override IOperation InstrumentedApply() {
IOperator successor = null;
if (ActualProbabilitiesParameter.ActualValue == null) {
ActualProbabilitiesParameter.Value = ProbabilitiesParameter.ActualValue.Clone() as DoubleArray;
} else {
String key = "SuccessfulOffspringAnalyzer Results";
ResultCollection results = null;
IScope scope = ExecutionContext.Parent.Scope;
int depth = 1;
while (scope != null && depth < DepthParameter.Value.Value) {
scope = scope.Parent;
depth++;
}
if (scope != null)
results = scope.Variables["Results"].Value as ResultCollection;
if (results != null && results.ContainsKey(key)) {
ResultCollection successProgressAnalysisResult = results[key].Value as ResultCollection;
key = SuccessProgressAnalyisis.Value.Value;
if (successProgressAnalysisResult.ContainsKey(key)) {
DataTable successProgressAnalysis = successProgressAnalysisResult[key].Value as DataTable;
for (int i = 0; i < Operators.Count; i++) {
IOperator current = Operators[i];
if (successProgressAnalysis.Rows.ContainsKey(current.Name)) {
DataRow row = successProgressAnalysis.Rows[current.Name];
double sum = 0.0;
ObservableList usages = row.Values;
sum += usages.Last();
ActualProbabilitiesParameter.ActualValue[i] += (sum / ActualProbabilitiesParameter.ActualValue[i]) * Factor.Value.Value;
}
}
}
}
//normalize
double max = ActualProbabilitiesParameter.ActualValue.Max();
for (int i = 0; i < ActualProbabilitiesParameter.ActualValue.Length; i++) {
ActualProbabilitiesParameter.ActualValue[i] /= max;
ActualProbabilitiesParameter.ActualValue[i] =
Math.Max(LowerBoundParameter.Value.Value,
ActualProbabilitiesParameter.ActualValue[i]);
}
}
//////////////// code has to be duplicated since ActualProbabilitiesParameter.ActualValue are updated and used for operator selection
IRandom random = RandomParameter.ActualValue;
DoubleArray probabilities = ActualProbabilitiesParameter.ActualValue;
if (probabilities.Length != Operators.Count) {
throw new InvalidOperationException(Name + ": The list of probabilities has to match the number of operators");
}
var checkedOperators = Operators.CheckedItems;
if (checkedOperators.Count() > 0) {
// select a random operator from the checked operators
successor =
checkedOperators.SampleProportional(random, 1, checkedOperators.Select(x => probabilities[x.Index]), false, false).First().Value;
}
IOperation successorOp = null;
if (Successor != null)
successorOp = ExecutionContext.CreateOperation(Successor);
OperationCollection next = new OperationCollection(successorOp);
if (successor != null) {
SelectedOperatorParameter.ActualValue = new StringValue(successor.Name);
if (CreateChildOperation)
next.Insert(0, ExecutionContext.CreateChildOperation(successor));
else next.Insert(0, ExecutionContext.CreateOperation(successor));
} else {
SelectedOperatorParameter.ActualValue = new StringValue("");
}
return next;
}
}
}