Posts for the month of July 2017

Windows 10 Creators Update: Problems Resolved

Apparently Microsoft has provided a fix for the blue screen issues that we described in May, see Problems with the Windows 10 Creators Update.

The update KB4022716 released on June, 27th includes the fix. After applying this update through windows update you can restore the nesting level to a more comfortable setting, e.g. 35. Changing the nesting level is described in this post for the current stable binaries and for the forthcoming 3.3.15 release version.

Users of the 3.3.14 release version cannot change the nesting level as the limit was hardcoded. However its setting can also be considered safe again after update KB4022716 is installed.

  • Posted: 2017-07-28 09:33 (Updated: 2017-08-03 23:49)
  • Author: Andreas Beham
  • Categories: (none)
  • Comments (0)

Implementing LAHC in HeuristicLab

Late Acceptance Hill-Climbing (LAHC) is a relatively recent single-solution metaheuristic, see Burke and Bykov's article:

Edmund K. Burke, Yuri Bykov, The late acceptance Hill-Climbing heuristic, European Journal of Operational Research, Volume 258, Issue 1, 2017, Pages 70-78.

It has a single parameter that controls the chance to make a deteroriating move. The higher this parameter the greater the chance for exploration and thus of reaching a higher quality solution, however the more time is required in convergence. LAHC is not available in HeuristicLab as a standard algorithm yet. But HeuristicLab makes it very easy to implement new algorithms through scripting and in doing so we can reuse the problem instances that we include as well as the infrastructure for visualizing solutions and analyzing runs. The following is a C# script-based implementation of LAHC that optimizes solutions to the berlin52 TSP problem.

If you copy and paste the following code to a new C# script you can run the algorithm. In this code you also see how you can create run objects from scripts. The run contains the solution, algorithm and problem description and convergence graphs among other data. If you put this and other run objects in a RunCollection you can easily do a performance analysis. Hint: You must obtain the latest stable binaries from our download page to run this code.

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;

using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Problems.Instances;
using HeuristicLab.Problems.Instances.TSPLIB;
using HeuristicLab.Problems.TravelingSalesman;
using HeuristicLab.Optimization;

public class LAHCScript : HeuristicLab.Scripting.CSharpScriptBase {
  public override void Main() {
    var random = new HeuristicLab.Random.MersenneTwister();
    var tsplib = new TSPLIBTSPInstanceProvider();
    var instance = tsplib.GetDataDescriptors()
      .Single(x => x.Name == "berlin52");
    var tsp = tsplib.LoadData(instance);
    var dm = new DistanceMatrix(tsp.GetDistanceMatrix());
    Permutation best = null, current = null, candidate = null;
    var candidateFitness = double.MaxValue;
    var bestFitness = double.MaxValue;
    var currentFitness = double.MaxValue;

    IsBetter isBetter = (first, second, orEqual) => (first < second)
      || (orEqual && first == second);
    InitializeFitness init = () => {
      candidate = new Permutation(
        PermutationTypes.RelativeUndirected, tsp.Dimension, random);
      current = candidate;
      best = candidate;
      candidateFitness = TourLength(candidate, dm);
      currentFitness = candidateFitness;
      bestFitness = candidateFitness;
      return candidateFitness;
    NextFitness next = () => {
      candidate = new Permutation(
        PermutationTypes.RelativeUndirected, current);
      var move = StochasticInversionSingleMoveGenerator
                   .Apply(candidate, random);
      candidateFitness = currentFitness + TSPInversionMovePathEvaluator
                   .EvaluateByDistanceMatrix(candidate, move, dm);
      // important to perform the move after the delta calculation
      InversionManipulator.Apply(candidate, move.Index1, move.Index2);
      return candidateFitness;
    Accept accept = () => {
      current = candidate;
      currentFitness = candidateFitness;
      if (isBetter(candidateFitness, bestFitness, orEqual: false)) {
        best = candidate;
        bestFitness = candidateFitness;
    var run = LAHC(init, next, accept, isBetter, 5000);
    run.Parameters.Add("Problem Name",
      new StringValue(instance.Name));
    run.Parameters.Add("Problem Type",
      new StringValue(typeof(TravelingSalesmanProblem).Name));
    run.Parameters.Add("Maximization", new BoolValue(false));
    if (tsp.BestKnownQuality.HasValue)
        new DoubleValue(tsp.BestKnownQuality.Value));
    var solution = new PathTSPTour(new DoubleMatrix(tsp.Coordinates),
                                 best, new DoubleValue(bestFitness));
    run.Results.Add("Best Solution", solution);
  private static double TourLength(Permutation perm, DistanceMatrix dm) {
    var length = dm[perm.Last(), perm.First()];
    for (var i = 1; i < perm.Length; i++) {
      length += dm[perm[i - 1], perm[i]];
    return length;
  public delegate double InitializeFitness();
  public delegate double NextFitness();
  public delegate void Accept();
  public delegate bool IsBetter(double first, double second, bool orEqual);
  public static Run LAHC(InitializeFitness init, NextFitness next,
      Accept accept, IsBetter isBetter, int listSize,
      int minTries = 100000) {
    var sw = Stopwatch.StartNew();
    var cgraph_fe = new IndexedDataTable<double>("Convergence Graph FE");
    var cgraph_wc = new IndexedDataTable<double>("Convergence Graph Time");
    var crow_fe = new IndexedDataRow<double>("First-hit");
    var crow_wc = new IndexedDataRow<double>("First-hit");
    cgraph_fe.VisualProperties.XAxisLogScale = true;
    cgraph_wc.VisualProperties.XAxisLogScale = true;
    var bestFit = init();
    var globalIter = 0;
    var memory = new double[listSize];
    for (var v = 0; v < listSize; v++) {
      memory[v] = bestFit;
    Tuple<int, double> last = null;
    foreach (var s in
        LAHC(next, accept, isBetter, bestFit, memory, minTries)) {
      if (isBetter(s.Item2, bestFit, orEqual: false)) {
        bestFit = s.Item2;
          Tuple.Create((double)globalIter + s.Item1, s.Item2));
          Tuple.Create(sw.ElapsedTicks / (double)Stopwatch.Frequency,
      last = s;
    globalIter += last.Item1;
    crow_fe.Values.Add(Tuple.Create((double)globalIter, bestFit));
      Tuple.Create(sw.ElapsedTicks / (double)Stopwatch.Frequency,
    var run = new Run() { Name = "LAHC-" + listSize };
    run.Parameters.Add("Algorithm Name", new StringValue("LAHC"));
    run.Parameters.Add("ListSize", new IntValue(listSize));
    run.Parameters.Add("MinTries", new IntValue(minTries));
    run.Parameters.Add("Algorithm Type", new StringValue("LAHC"));
    run.Results.Add("ExecutionTime", new TimeSpanValue(
        / (double)Stopwatch.Frequency)));
    run.Results.Add("EvaluatedSolutions", new IntValue(globalIter));
    run.Results.Add("BestQuality", new DoubleValue(bestFit));
    run.Results.Add("QualityPerEvaluations", cgraph_fe);
    run.Results.Add("QualityPerClock", cgraph_wc);
    return run;
  private static IEnumerable<Tuple<int, double>> LAHC(
      NextFitness nextFitness, Accept doAccept,
      IsBetter isBetter, double fit, double[] memory,
      int minTries) {
    var bestFit = fit;
    var tries = 0;
    var lastSuccess = 0;
    var l = memory.Length;
    while (tries < minTries || (tries - lastSuccess) < tries * 0.02) {
      var nextFit = nextFitness();
      var v = tries % l;
      var prevFit = memory[v];
      var accept = isBetter(nextFit, fit, orEqual: true)
                || isBetter(nextFit, prevFit, orEqual: true);
      if (accept && isBetter(nextFit, fit, orEqual: false))
        lastSuccess = tries;
      if (accept) {
        if (isBetter(nextFit, bestFit, orEqual: false)) {
          bestFit = nextFit;
          yield return Tuple.Create(tries, bestFit);
        fit = nextFit;
      if (isBetter(fit, prevFit, orEqual: false)) 
        memory[v] = fit;
    yield return Tuple.Create(tries, bestFit);