#region License Information
/* HeuristicLab
* Copyright (C) 2002-2016 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 System.Threading;
using HeuristicLab.Algorithms.MemPR.Interfaces;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Random;
namespace HeuristicLab.Algorithms.MemPR.Binary {
[Item("MemPR (binary)", "MemPR implementation for binary vectors.")]
[StorableClass]
[Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 999)]
public class BinaryMemPR : MemPRAlgorithm, BinaryVector, BinaryMemPRPopulationContext, BinaryMemPRSolutionContext> {
[StorableConstructor]
protected BinaryMemPR(bool deserializing) : base(deserializing) { }
protected BinaryMemPR(BinaryMemPR original, Cloner cloner) : base(original, cloner) { }
public BinaryMemPR() {
foreach (var trainer in ApplicationManager.Manager.GetInstances>())
SolutionModelTrainerParameter.ValidValues.Add(trainer);
foreach (var localSearch in ApplicationManager.Manager.GetInstances>()) {
LocalSearchParameter.ValidValues.Add(localSearch);
}
}
public override IDeepCloneable Clone(Cloner cloner) {
return new BinaryMemPR(this, cloner);
}
protected override bool Eq(ISingleObjectiveSolutionScope a, ISingleObjectiveSolutionScope b) {
var len = a.Solution.Length;
var acode = a.Solution;
var bcode = b.Solution;
for (var i = 0; i < len; i++)
if (acode[i] != bcode[i]) return false;
return true;
}
protected override double Dist(ISingleObjectiveSolutionScope a, ISingleObjectiveSolutionScope b) {
return 1.0 - HammingSimilarityCalculator.CalculateSimilarity(a.Solution, b.Solution);
}
protected override ISingleObjectiveSolutionScope ToScope(BinaryVector code, double fitness = double.NaN) {
var creator = Problem.SolutionCreator as IBinaryVectorCreator;
if (creator == null) throw new InvalidOperationException("Can only solve binary encoded problems with MemPR (binary)");
return new SingleObjectiveSolutionScope(code, creator.BinaryVectorParameter.ActualName, fitness, Problem.Evaluator.QualityParameter.ActualName) {
Parent = Context.Scope
};
}
protected override ISolutionSubspace CalculateSubspace(IEnumerable solutions, bool inverse = false) {
var pop = solutions.ToList();
var N = pop[0].Length;
var subspace = new bool[N];
for (var i = 0; i < N; i++) {
var val = pop[0][i];
if (inverse) subspace[i] = true;
for (var p = 1; p < pop.Count; p++) {
if (pop[p][i] != val) subspace[i] = !inverse;
}
}
return new BinarySolutionSubspace(subspace);
}
protected override void AdaptiveWalk(ISingleObjectiveSolutionScope scope, int maxEvals, CancellationToken token, ISolutionSubspace subspace = null) {
var evaluations = 0;
var subset = subspace != null ? ((BinarySolutionSubspace)subspace).Subspace : null;
if (double.IsNaN(scope.Fitness)) {
Evaluate(scope, token);
evaluations++;
}
SingleObjectiveSolutionScope bestOfTheWalk = null;
var currentScope = (SingleObjectiveSolutionScope)scope.Clone();
var current = currentScope.Solution;
var N = current.Length;
var subN = subset != null ? subset.Count(x => x) : N;
if (subN == 0) return;
var order = Enumerable.Range(0, N).Where(x => subset == null || subset[x]).Shuffle(Context.Random).ToArray();
var max = Context.Population.Max(x => x.Fitness);
var min = Context.Population.Min(x => x.Fitness);
var range = (max - min);
if (range.IsAlmost(0)) range = Math.Abs(max * 0.05);
//else range += range * 0.05;
if (range.IsAlmost(0)) { // because min = max = 0
Context.IncrementEvaluatedSolutions(evaluations);
return;
}
//var upperBound = Problem.Maximization ? min - range : max + range;
//var lowerBound = Problem.Maximization ? max : min;
var temp = 1.0;
for (var iter = 0; iter < int.MaxValue; iter++) {
var moved = false;
for (var i = 0; i < subN; i++) {
var idx = order[i];
var before = currentScope.Fitness;
current[idx] = !current[idx];
Evaluate(currentScope, token);
evaluations++;
var after = currentScope.Fitness;
if (Context.IsBetter(after, before) && (bestOfTheWalk == null || Context.IsBetter(after, bestOfTheWalk.Fitness))) {
bestOfTheWalk = (SingleObjectiveSolutionScope)currentScope.Clone();
}
var diff = Problem.Maximization ? after - before : before - after;
if (diff > 0) moved = true;
else {
var prob = Math.Exp(temp * diff / range);
if (Context.Random.NextDouble() >= prob) {
// the move is not good enough -> undo the move
current[idx] = !current[idx];
currentScope.Fitness = before;
}
}
temp += 100.0 / maxEvals;
if (evaluations >= maxEvals) break;
}
if (!moved) break;
if (evaluations >= maxEvals) break;
}
Context.IncrementEvaluatedSolutions(evaluations);
scope.Adopt(bestOfTheWalk ?? currentScope);
}
protected override ISingleObjectiveSolutionScope Breed(ISingleObjectiveSolutionScope p1, ISingleObjectiveSolutionScope p2, CancellationToken token) {
var evaluations = 0;
var N = p1.Solution.Length;
if (double.IsNaN(p1.Fitness)) {
Evaluate(p1, token);
evaluations++;
}
if (double.IsNaN(p2.Fitness)) {
Evaluate(p2, token);
evaluations++;
}
var better = Problem.Maximization ? Math.Max(p1.Fitness, p2.Fitness)
: Math.Min(p1.Fitness, p2.Fitness);
var offspring = ToScope(null);
var probe = ToScope(new BinaryVector(N));
var order = Enumerable.Range(0, N - 1).Shuffle(Context.Random).ToList();
for (var x = 0; x < N - 1; x++) {
var b = order[x];
if (p1.Solution[b] == p2.Solution[b]) continue;
for (var i = 0; i <= b; i++)
probe.Solution[i] = p1.Solution[i];
for (var i = b + 1; i < probe.Solution.Length; i++)
probe.Solution[i] = p2.Solution[i];
Evaluate(probe, token);
evaluations++;
if (Context.IsBetter(probe, offspring)) {
if (Context.IsBetter(probe.Fitness, better)) {
Context.IncrementEvaluatedSolutions(evaluations);
return probe;
}
offspring = (ISingleObjectiveSolutionScope)probe.Clone();
}
}
while (evaluations < Context.LocalSearchEvaluations) {
probe.Solution = UniformCrossover.Apply(Context.Random, p1.Solution, p2.Solution);
Evaluate(probe, token);
evaluations++;
if (Context.IsBetter(probe, offspring)) {
if (Context.IsBetter(probe.Fitness, better)) {
Context.IncrementEvaluatedSolutions(evaluations);
return probe;
}
offspring = (ISingleObjectiveSolutionScope)probe.Clone();
}
}
Context.IncrementEvaluatedSolutions(evaluations);
return offspring;
}
protected override ISingleObjectiveSolutionScope Link(ISingleObjectiveSolutionScope a, ISingleObjectiveSolutionScope b, CancellationToken token, bool delink = false) {
var evaluations = 0;
if (double.IsNaN(a.Fitness)) {
Evaluate(a, token);
evaluations++;
}
if (double.IsNaN(b.Fitness)) {
Evaluate(b, token);
evaluations++;
}
var childScope = (ISingleObjectiveSolutionScope)a.Clone();
var child = childScope.Solution;
ISingleObjectiveSolutionScope best = null;
var cF = a.Fitness;
var bF = double.NaN;
var order = Enumerable.Range(0, child.Length)
.Where(x => !delink && child[x] != b.Solution[x] || delink && child[x] == b.Solution[x])
.Shuffle(Context.Random).ToList();
if (order.Count == 0) return childScope;
while (true) {
var bestS = double.NaN;
var bestI = -1;
for (var i = 0; i < order.Count; i++) {
var idx = order[i];
child[idx] = !child[idx]; // move
Evaluate(childScope, token);
evaluations++;
var s = childScope.Fitness;
childScope.Fitness = cF;
child[idx] = !child[idx]; // undo move
if (Context.IsBetter(s, cF)) {
bestS = s;
bestI = i;
break; // first-improvement
}
if (Context.IsBetter(s, bestS)) {
// least-degrading
bestS = s;
bestI = i;
}
}
child[order[bestI]] = !child[order[bestI]];
order.RemoveAt(bestI);
cF = bestS;
childScope.Fitness = cF;
if (Context.IsBetter(cF, bF)) {
bF = cF;
best = (ISingleObjectiveSolutionScope)childScope.Clone();
}
if (order.Count == 0) break;
}
Context.IncrementEvaluatedSolutions(evaluations);
return best ?? childScope;
}
}
}