source: branches/Parameter-less Population Pyramid/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/HillClimber.cs @ 11637

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;


namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
  [Item("Hill Climber", "Test algorithm.")]
  [StorableClass]
  [Creatable("Parameterless Population Pyramid")]
  public class HillClimber : AlgorithmBase {
    [Storable]
    private IRandom random;

    [StorableConstructor]
    protected HillClimber(bool deserializing) : base(deserializing) { }
    protected HillClimber(HillClimber original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new HillClimber(this, cloner);
    }

    public HillClimber()
      : base() {
      random = new MersenneTwister();
    }
    protected override void Run() {
      bool[] solution = new bool[Problem.Length];
      for (int i = 0; i < solution.Length; i++) {
        solution[i] = random.Next(2) == 1;
      }
      var fitness = Problem.Evaluate(solution);
      fitness = ImproveToLocalOptimum(Problem, solution, fitness, random);

      Results.Add(new Result("Best quality", new DoubleValue(fitness)));
      Results.Add(new Result("Best solution", new BinaryVector(solution)));
    }
    public static double ImproveToLocalOptimum(BinaryVectorProblem problem, bool[] solution, double fitness, IRandom rand) {
      var tried = new HashSet<int>();
      do {
        var options = Enumerable.Range(0, solution.Length).Shuffle(rand);
        foreach (var option in options) {
          solution[option] = !solution[option];
          double newFitness = problem.Evaluate(solution);
          if ((problem.Maximization && fitness < newFitness) || (!problem.Maximization && fitness > newFitness)) {
            fitness = newFitness;
            tried.Clear();
          } else {
            solution[option] = !solution[option];
          }
          tried.Add(option);
        }
      } while (tried.Count != solution.Length);
      return fitness;
    }
  }
}