#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 . */ #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 { public override bool Maximization { get { return true; } } public OneMaxNew() : base() { Encoding = new BinaryEncoding("BinaryVector", 10); var bestScopeSolutionAnalyzer = Operators.OfType().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(); } } }