[12897] | 1 | #region License Information
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| 2 |
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| 3 | /* HeuristicLab
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[17181] | 4 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[12897] | 5 | *
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| 6 | * This file is part of HeuristicLab.
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| 7 | *
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| 8 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 9 | * it under the terms of the GNU General Public License as published by
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| 10 | * the Free Software Foundation, either version 3 of the License, or
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| 11 | * (at your option) any later version.
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| 12 | *
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| 13 | * HeuristicLab is distributed in the hope that it will be useful,
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| 14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 16 | * GNU General Public License for more details.
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| 17 | *
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| 18 | * You should have received a copy of the GNU General Public License
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| 19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 20 | */
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| 21 |
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| 22 | #endregion
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| 23 |
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[12904] | 24 | using System;
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[12897] | 25 | using System.Linq;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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[13280] | 28 | using HeuristicLab.Data;
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[12897] | 29 | using HeuristicLab.Optimization;
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[17097] | 30 | using HEAL.Attic;
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[12897] | 31 |
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| 32 | namespace HeuristicLab.Encodings.SymbolicExpressionTreeEncoding {
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[17097] | 33 | [StorableType("A1B9F4C8-5E29-493C-A483-2AC68453BC63")]
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[12897] | 34 | public abstract class SymbolicExpressionTreeProblem : SingleObjectiveBasicProblem<SymbolicExpressionTreeEncoding> {
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| 35 |
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| 36 | // persistence
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| 37 | [StorableConstructor]
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[17097] | 38 | protected SymbolicExpressionTreeProblem(StorableConstructorFlag _) : base(_) { }
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[12897] | 39 | [StorableHook(HookType.AfterDeserialization)]
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| 40 | private void AfterDeserialization() { }
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| 41 |
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| 42 |
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| 43 | // cloning
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| 44 | protected SymbolicExpressionTreeProblem(SymbolicExpressionTreeProblem original, Cloner cloner)
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| 45 | : base(original, cloner) {
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| 46 | }
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| 47 |
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| 48 | protected SymbolicExpressionTreeProblem() : base() { }
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| 49 |
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| 50 | public virtual bool IsBetter(double quality, double bestQuality) {
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| 51 | return (Maximization && quality > bestQuality || !Maximization && quality < bestQuality);
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| 52 | }
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| 53 |
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[12904] | 54 | public abstract double Evaluate(ISymbolicExpressionTree tree, IRandom random);
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[12897] | 55 | public sealed override double Evaluate(Individual individual, IRandom random) {
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| 56 | return Evaluate(individual.SymbolicExpressionTree(), random);
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| 57 | }
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| 58 |
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[13280] | 59 | public virtual void Analyze(ISymbolicExpressionTree[] trees, double[] qualities, ResultCollection results,
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| 60 | IRandom random) {
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| 61 | if (!results.ContainsKey("Best Solution Quality")) {
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| 62 | results.Add(new Result("Best Solution Quality", typeof(DoubleValue)));
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| 63 | }
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[12897] | 64 | if (!results.ContainsKey("Best Solution")) {
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| 65 | results.Add(new Result("Best Solution", typeof(ISymbolicExpressionTree)));
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| 66 | }
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[13280] | 67 |
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| 68 | var bestQuality = Maximization ? qualities.Max() : qualities.Min();
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| 69 |
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| 70 | if (results["Best Solution Quality"].Value == null ||
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| 71 | IsBetter(bestQuality, ((DoubleValue)results["Best Solution Quality"].Value).Value)) {
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| 72 | var bestIdx = Array.IndexOf(qualities, bestQuality);
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| 73 | var bestClone = (IItem)trees[bestIdx].Clone();
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| 74 | results["Best Solution"].Value = bestClone;
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| 75 | results["Best Solution Quality"].Value = new DoubleValue(bestQuality);
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| 76 | }
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[12897] | 77 | }
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[13280] | 78 |
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[12904] | 79 | public sealed override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
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| 80 | Analyze(individuals.Select(ind => ind.SymbolicExpressionTree()).ToArray(), qualities, results, random);
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| 81 | }
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[12897] | 82 | }
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| 83 | }
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