source: branches/ProgrammableProblem/HeuristicLab.Problems.Programmable/3.3/New/OneMaxNew.cs @ 11813

#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.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.Programmable {
  [Item("One Max New", "Represents a single-objective problem that can be programmed.")]
  [Creatable("1 Test")]
  [StorableClass]
  public class OneMaxNew : SingleObjectiveProgrammableProblem<BinaryEncoding> {
    public override bool Maximization {
      get { return true; }
    }

    public OneMaxNew()
      : base() {
      Encoding = new BinaryEncoding("BinaryVector", 10);
      var bestScopeSolutionAnalyzer = Operators.OfType<BestScopeSolutionAnalyzer>().FirstOrDefault();
      if (bestScopeSolutionAnalyzer != null) Operators.Remove(bestScopeSolutionAnalyzer);
    }

    [StorableConstructor]
    protected OneMaxNew(bool deserializing) : base(deserializing) { }

    protected OneMaxNew(OneMaxNew original, Cloner cloner) : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new OneMaxNew(this, cloner);
    }
    public override double Evaluate(Individual individual, IRandom random) {
      return individual.BinaryVector().Count(b => b);
    }

    public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results) {
      base.Analyze(individuals, qualities, results);
      var best = individuals.Zip(qualities, (i, q) => new { Individual = i, Quality = q }).OrderByDescending(z => z.Quality).First();
      if (!results.ContainsKey("Best Solution")) {
        results.Add(new Result("Best Solution", typeof(BinaryVector)));
      }
      results["Best Solution"].Value = best.Individual.BinaryVector();
    }
  }
}