source: misc/tools/HeuristicLab.Benchmarks/HeuristicLab.Benchmarks/Encodings.SymbolicExpressionTree/SubtreeCrossoverPerformance.cs @ 16300

using System;
using System.Collections.Generic;
using BenchmarkDotNet.Attributes;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Random;

namespace HeuristicLab.Benchmarks {
  [CoreJob, ClrJob]
  [MinColumn, MaxColumn, MeanColumn, MedianColumn]
  public class SubtreeCrossoverPerformance {

    private List<ISymbolicExpressionTree> trees = new List<ISymbolicExpressionTree>();
    private readonly IRandom random = new MersenneTwister(1234);
    private readonly ISymbolicExpressionGrammar grammar = Grammars.CreateArithmeticAndAdfGrammar();

    [GlobalSetup]
    public void Setup() {
      for (int i = 0; i < PopulationSize; i++) {
        trees.Add(ProbabilisticTreeCreator.Create(random, grammar, MaxLength, MaxDepth));
        for (int j = random.Next(3); j < 3; j++)
          SubroutineCreater.CreateSubroutine(random, trees[i], MaxLength, MaxDepth, 3, 3);
      }
    }

    [Params(100)]
    public int MaxLength { get; set; }

    [Params(10)]
    public int MaxDepth { get; set; }

    [Params(1_000)]
    public int PopulationSize { get; set; }

    [Params(5)]
    public int Generations { get; set; }

    [Benchmark]
    public void StandardCrossover() => TestCrossoverPerformance(StandardCrossover);

    #region helper methods
    private void StandardCrossover(IRandom random, ISymbolicExpressionTree par0, ISymbolicExpressionTree par1, double internalProb, int maxLength, int maxDepth)
      => SubtreeCrossover.Cross(random, par0, par1, internalProb, maxLength, maxDepth);


    private void TestCrossoverPerformance(Action<IRandom, ISymbolicExpressionTree, ISymbolicExpressionTree, double, int, int> crossover) {
      for (int gCount = 0; gCount < Generations; gCount++) {
        for (int i = 0; i < PopulationSize; i++) {
          var par0 = (ISymbolicExpressionTree)trees.SampleRandom(random).Clone();
          var par1 = (ISymbolicExpressionTree)trees.SampleRandom(random).Clone();
          crossover(random, par0, par1, 0.9, 100, 10);
        }
      }
    }

    #endregion
  }
}