source: branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Algorithms.GeneticProgramming/StandardGP.cs @ 11851

using System;
using System.Linq;
using System.Threading;
using HeuristicLab.Algorithms.GrammaticalOptimization;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Problems.GrammaticalOptimization;
using HeuristicLab.Selection;
using HeuristicLab.Algorithms.GeneticAlgorithm;

namespace HeuristicLab.Algorithms.GeneticProgramming {
  public class StandardGP : SolverBase {
    public int PopulationSize { get; set; }
    public double MutationRate { get; set; }
    public int TournamentGroupSize { get; set; }
    public int MaxSolutionSize { get; set; }
    public int MaxSolutionDepth { get; set; }

    private readonly ISymbolicExpressionTreeProblem problem;
    private readonly Random random;

    public StandardGP(ISymbolicExpressionTreeProblem problem, Random random) {
      this.problem = problem;
      this.random = random;
      // default parameter values
      PopulationSize = 1000;
      TournamentGroupSize = 7;
      MutationRate = 0.15;
      MaxSolutionSize = 100;
      MaxSolutionDepth = 17;
    }

    public override void Run(int maxEvaluations) {
      var hlProblem = new GenericSymbExprProblem(problem);
      var onEvalLocker = new object();
      hlProblem.Evaluator.SolutionEvaluated += (sentence, quality) => {
        // raise solution evaluated event for each GP solution, don't scale quality to 0..1 
        // need to synchronize in case we are using a parallel engine
        lock (onEvalLocker) {
          OnSolutionEvaluated(sentence, quality);
        }
      };
      hlProblem.MaximumSymbolicExpressionTreeLength.Value = MaxSolutionSize;
      hlProblem.MaximumSymbolicExpressionTreeDepth.Value = MaxSolutionDepth;


      using (var wh = new AutoResetEvent(false)) {
        var ga = new GeneticAlgorithm.GeneticAlgorithm();
        ga.Engine = new ParallelEngine.ParallelEngine();
        ga.ExceptionOccurred += (sender, args) => { Console.WriteLine(args.Value.Message); wh.Set(); };
        ga.Stopped += (sender, args) => { wh.Set(); };

        ga.Problem = hlProblem;
        var mutator = (MultiSymbolicExpressionTreeManipulator)ga.MutatorParameter.ValidValues.Single(op => op.Name == "MultiSymbolicExpressionTreeManipulator");
        foreach (var op in mutator.Operators) {
          if (op.Name == "ChangeNodeTypeManipulation"
            || op.Name == "ReplaceBranchManipulation") mutator.Operators.SetItemCheckedState(op, true);
          else mutator.Operators.SetItemCheckedState(op, false);
        }
        ga.Mutator = mutator;
        ga.Crossover = ga.CrossoverParameter.ValidValues.Single(op => op.Name == "SubtreeSwappingCrossover");
        var selector = (TournamentSelector)ga.SelectorParameter.ValidValues.Single(op => op.Name == "TournamentSelector");
        selector.GroupSizeParameter.Value = new IntValue(TournamentGroupSize);
        ga.Selector = selector;

        ga.PopulationSize.Value = PopulationSize;
        ga.MaximumGenerations.Value = maxEvaluations / PopulationSize + 1; // one extra generation in case maxEvaluations is not a multiple of PopulationSize
        ga.MutationProbability.Value = MutationRate;

        ga.SetSeedRandomly = new BoolValue(false);
        ga.Seed = new IntValue(random.Next());

        ga.Prepare();
        ga.Start();

        wh.WaitOne();
      }
    }
  }
}