[11846] | 1 | using System;
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| 2 | using System.Linq;
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| 3 | using System.Threading;
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| 4 | using HeuristicLab.Algorithms.GrammaticalOptimization;
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| 5 | using HeuristicLab.Data;
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| 6 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 7 | using HeuristicLab.Optimization;
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| 8 | using HeuristicLab.Problems.GrammaticalOptimization;
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| 9 | using HeuristicLab.Selection;
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| 10 | using HeuristicLab.Algorithms.GeneticAlgorithm;
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| 11 |
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| 12 | namespace HeuristicLab.Algorithms.GeneticProgramming {
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| 13 | public class StandardGP : SolverBase {
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| 14 | public int PopulationSize { get; set; }
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| 15 | public double MutationRate { get; set; }
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| 16 | public int TournamentGroupSize { get; set; }
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| 17 | public int MaxSolutionSize { get; set; }
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| 18 | public int MaxSolutionDepth { get; set; }
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| 19 |
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| 20 | private readonly ISymbolicExpressionTreeProblem problem;
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| 21 | private readonly Random random;
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| 22 |
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| 23 | public StandardGP(ISymbolicExpressionTreeProblem problem, Random random) {
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| 24 | this.problem = problem;
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| 25 | this.random = random;
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| 26 | // default parameter values
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| 27 | PopulationSize = 1000;
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| 28 | TournamentGroupSize = 7;
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| 29 | MutationRate = 0.15;
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| 30 | MaxSolutionSize = 100;
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| 31 | MaxSolutionDepth = 17;
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| 32 | }
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| 33 |
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| 34 | public override void Run(int maxEvaluations) {
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| 35 | var hlProblem = new GenericSymbExprProblem(problem);
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[11851] | 36 | var onEvalLocker = new object();
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| 37 | hlProblem.Evaluator.SolutionEvaluated += (sentence, quality) => {
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| 38 | // raise solution evaluated event for each GP solution, don't scale quality to 0..1
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| 39 | // need to synchronize in case we are using a parallel engine
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| 40 | lock (onEvalLocker) {
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| 41 | OnSolutionEvaluated(sentence, quality);
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| 42 | }
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| 43 | };
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[11847] | 44 | hlProblem.MaximumSymbolicExpressionTreeLength.Value = MaxSolutionSize;
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| 45 | hlProblem.MaximumSymbolicExpressionTreeDepth.Value = MaxSolutionDepth;
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[11846] | 46 |
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[11847] | 47 |
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[11846] | 48 | using (var wh = new AutoResetEvent(false)) {
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| 49 | var ga = new GeneticAlgorithm.GeneticAlgorithm();
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[11847] | 50 | ga.Engine = new ParallelEngine.ParallelEngine();
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[11846] | 51 | ga.ExceptionOccurred += (sender, args) => { Console.WriteLine(args.Value.Message); wh.Set(); };
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| 52 | ga.Stopped += (sender, args) => { wh.Set(); };
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| 53 |
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| 54 | ga.Problem = hlProblem;
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| 55 | var mutator = (MultiSymbolicExpressionTreeManipulator)ga.MutatorParameter.ValidValues.Single(op => op.Name == "MultiSymbolicExpressionTreeManipulator");
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| 56 | foreach (var op in mutator.Operators) {
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| 57 | if (op.Name == "ChangeNodeTypeManipulation"
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| 58 | || op.Name == "ReplaceBranchManipulation") mutator.Operators.SetItemCheckedState(op, true);
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| 59 | else mutator.Operators.SetItemCheckedState(op, false);
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| 60 | }
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| 61 | ga.Mutator = mutator;
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| 62 | ga.Crossover = ga.CrossoverParameter.ValidValues.Single(op => op.Name == "SubtreeSwappingCrossover");
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| 63 | var selector = (TournamentSelector)ga.SelectorParameter.ValidValues.Single(op => op.Name == "TournamentSelector");
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| 64 | selector.GroupSizeParameter.Value = new IntValue(TournamentGroupSize);
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| 65 | ga.Selector = selector;
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| 66 |
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| 67 | ga.PopulationSize.Value = PopulationSize;
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| 68 | ga.MaximumGenerations.Value = maxEvaluations / PopulationSize + 1; // one extra generation in case maxEvaluations is not a multiple of PopulationSize
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| 69 | ga.MutationProbability.Value = MutationRate;
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| 70 |
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| 71 | ga.SetSeedRandomly = new BoolValue(false);
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| 72 | ga.Seed = new IntValue(random.Next());
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| 73 |
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| 74 | ga.Prepare();
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| 75 | ga.Start();
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| 76 |
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| 77 | wh.WaitOne();
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| 78 | }
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| 79 | }
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| 80 | }
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| 81 | }
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