public static void Run()
{
var config = new EAConfiguration
{
PopulationSize = 13,
OverproductionFactor = 1.5,
MaximumGenerations = 10000,
CrossoverType = CrossoverType.Uniform,
AdultSelectionType = AdultSelectionType.Overproduction,
ParentSelectionType = ParentSelectionType.Tournament,
CrossoverRate = 0.21,
MutationRate = 0.98,
TournamentSize = 7,
TournamentProbability = 0.895,
TargetFitness = 1.0,
Mode = EAMode.MaximizeFitness,
Elites = 0,
CalculateStatistics = true
};
var lolzea = new LOLZEA(config, new DefaultRandomNumberGenerator());
var stopwatchtot = new Stopwatch();
var stopwatchgen = new Stopwatch();
PopulationStatistics currentStats = new PopulationStatistics();
lolzea.PopulationStatisticsCalculated += (stats) =>
{
currentStats = stats;
};
lolzea.NewGenerationEvent += (gen) => {
//PrintProgressBar(gen, config.MaximumGenerations);
var progress = (gen / (double)config.MaximumGenerations) * 100.0;
var totruntime = stopwatchtot.Elapsed;
var genruntime = stopwatchgen.Elapsed;
Console.WriteLine();
Console.WriteLine(string.Format("G# {0} best_f: {1:F3} avg_f: {2:F3} SD: {3:F3} Progress: {4,5:F2} Gen: {5} Tot: {6}", gen, lolzea.Best.Fitness, currentStats.AverageFitness, currentStats.StandardDeviationFitness, progress, genruntime, totruntime));
Console.WriteLine($"Generation winner: {lolzea.Best.ToString()}");
stopwatchgen.Restart();
};
stopwatchtot.Start();
stopwatchgen.Start();
var res = lolzea.Evolve();
Console.WriteLine("\n\nDone!");
Console.WriteLine($"Winner: {res.Winner}");
Console.Read();
}
public static void Run()
{
var config = new EAConfiguration
{
PopulationSize = 100,
OverproductionFactor = 2.0,
MaximumGenerations = 100,
CrossoverType = CrossoverType.OnePoint,
AdultSelectionType = AdultSelectionType.GenerationalMixing,
ParentSelectionType = ParentSelectionType.Tournament,
CrossoverRate = 0.9,
MutationRate = 0.01,
TournamentSize = 10,
TournamentProbability = 0.8,
TargetFitness = 1.0,
Mode = EAMode.MaximizeFitness,
Elites = 1,
CalculateStatistics = true
};
var onemaxEA = new OneMaxEA(config, new DefaultRandomNumberGenerator());
var stopwatchtot = new Stopwatch();
var stopwatchgen = new Stopwatch();
PopulationStatistics currentStats = new PopulationStatistics();
onemaxEA.PopulationStatisticsCalculated += (stats) =>
{
currentStats = stats;
};
onemaxEA.NewGenerationEvent += (gen) => {
//PrintProgressBar(gen, config.MaximumGenerations);
double progress = (gen / (double)config.MaximumGenerations) * 100.0;
var totruntime = stopwatchtot.Elapsed;
var genruntime = stopwatchgen.Elapsed;
Console.WriteLine();
Console.WriteLine(string.Format("G# {0} best_f: {1:F3} avg_f: {2:F3} SD: {3:F3} Progress: {4,5:F2} Gen: {5} Tot: {6}", gen, onemaxEA.Best.Fitness, currentStats.AverageFitness, currentStats.StandardDeviationFitness, progress, genruntime, totruntime));
Console.WriteLine("Generation winner: " + ((BinaryGenotype)onemaxEA.Best?.Genotype).ToBitString());
stopwatchgen.Restart();
};
stopwatchtot.Start();
stopwatchgen.Start();
var res = onemaxEA.Evolve();
Console.WriteLine("\n\nDone!");
Console.WriteLine($"Winner: {res.Winner}");
Console.Read();
}
public void TournamentParentSelection_VerifySelectionProbability()
{
// Test tournament selection by comparing it to the old "established" tournament selection implementation.
var random = new DefaultRandomNumberGenerator("seed".GetHashCode());
for (int tournamentSize = 2; tournamentSize <= 10; tournamentSize++)
{
var population = new Population(11);
var index = 0;
population.Fill(() => new TestPhenotype(index++, index * index));
population.Sort(EAMode.MaximizeFitness);
var config = new EAConfiguration
{
TournamentSize = 3,
TournamentProbability = 0.5
};
#pragma warning disable CS0618 // Type or member is obsolete
var oldselection = new TournamentParentSelectionOld(config);
#pragma warning restore CS0618 // Type or member is obsolete
var oldselected = oldselection.SelectParents(population, 275000, EAMode.MaximizeFitness, random);
foreach (var(a, b) in oldselected)
{
((TestPhenotype)a).Count2++;
((TestPhenotype)b).Count2++;
}
var selection = new TournamentParentSelection(config);
var selected = selection.SelectParents(population, 275000, EAMode.MaximizeFitness, random);
foreach (var(a, b) in selected)
{
((TestPhenotype)a).Count++;
((TestPhenotype)b).Count++;
}
var counts = population.Cast <TestPhenotype>().Select(p => p.Count).Zip(population.Cast <TestPhenotype>().Select(p => p.Count2), (c1, c2) => (c1, c2));
foreach (var count in counts)
{
(count.c1 - count.c2).Should().BeLessThan(1000, $"tournament size = {tournamentSize}");
}
}
}
public void ReadConfigFromFileTest()
{
var path = "TestFiles/TestConfig.json";
var config = EAConfiguration.ReadConfigurationFromFile(path);
config.PopulationSize.Should().Be(100);
config.OverproductionFactor.Should().Be(1.5);
config.MaximumGenerations.Should().Be(500);
config.CrossoverType.Should().Be(CrossoverType.Uniform);
config.AdultSelectionType.Should().Be(AdultSelectionType.Overproduction);
config.ParentSelectionType.Should().Be(ParentSelectionType.FitnessProportionate);
config.CrossoverRate.Should().Be(0.8);
config.MutationRate.Should().Be(0.02);
config.TournamentSize.Should().Be(10);
config.TournamentProbability.Should().Be(0.5);
config.TargetFitness.Should().Be(1.0);
config.Mode.Should().Be(EAMode.MaximizeFitness);
config.Elites.Should().Be(1);
config.ReevaluateElites.Should().BeTrue();
}
public void ReadConfigFromJsonStringTest()
{
var json =
@"{
""PopulationSize"": 100,
""OverproductionFactor"": 1.5,
""MaximumGenerations"": 500,
""CrossoverType"": ""Uniform"",
""AdultSelectionType"": ""Overproduction"",
""ParentSelectionType"": ""FitnessProportionate"",
""CrossoverRate"": 0.8,
""MutationRate"": 0.02,
""TournamentSize"": 10,
""TournamentProbability"": 0.5,
""TargetFitness"": 1.0,
""Mode"": ""MaximizeFitness"",
""Elites"": 1,
""ReevaluateElites"": true,
""RankSelectionMinProbability"": 0.5,
""RankSelectionMaxProbability"": 1.5
}";
var config = EAConfiguration.ParseFromJsonString(json);
config.PopulationSize.Should().Be(100);
config.OverproductionFactor.Should().Be(1.5);
config.MaximumGenerations.Should().Be(500);
config.CrossoverType.Should().Be(CrossoverType.Uniform);
config.AdultSelectionType.Should().Be(AdultSelectionType.Overproduction);
config.ParentSelectionType.Should().Be(ParentSelectionType.FitnessProportionate);
config.CrossoverRate.Should().Be(0.8);
config.MutationRate.Should().Be(0.02);
config.TournamentSize.Should().Be(10);
config.TournamentProbability.Should().Be(0.5);
config.TargetFitness.Should().Be(1.0);
config.Mode.Should().Be(EAMode.MaximizeFitness);
config.Elites.Should().Be(1);
config.ReevaluateElites.Should().BeTrue();
config.RankSelectionMinProbability.Should().Be(0.5);
config.RankSelectionMaxProbability.Should().Be(1.5);
}
public void TournamentSelection_PerfTest()
{
var random = new DefaultRandomNumberGenerator();
var config = new EAConfiguration
{
TournamentSize = 20,
TournamentProbability = 0.5
};
var popsize = 10000;
var pop = new Population(popsize);
pop.Fill(() => CreatePhenotypeMock(random.NextDouble() * 10).Object);
pop.Sort(EAMode.MaximizeFitness);
Console.WriteLine($"popsize = {popsize}, tournament size = {config.TournamentSize}");
var watch = new Stopwatch();
watch.Start();
#pragma warning disable CS0618 // Type or member is obsolete
IParentSelection selection = new TournamentParentSelectionOld(config);
#pragma warning restore CS0618 // Type or member is obsolete
foreach (var selected in selection.SelectParents(pop, popsize, EAMode.MaximizeFitness, random))
{
}
watch.Stop();
Console.WriteLine($"old: {watch.ElapsedMilliseconds} ms");
watch.Restart();
selection = new TournamentParentSelection(config);
foreach (var selected in selection.SelectParents(pop, popsize, EAMode.MaximizeFitness, random))
{
}
watch.Stop();
Console.WriteLine($"new: {watch.ElapsedMilliseconds} ms");
}
public void TestOneMaxEA()
{
var config = new EAConfiguration
{
PopulationSize = 100,
OverproductionFactor = 2.0,
MaximumGenerations = 100,
CrossoverType = CrossoverType.OnePoint,
AdultSelectionType = AdultSelectionType.GenerationalMixing,
ParentSelectionType = ParentSelectionType.Tournament,
CrossoverRate = 0.9,
MutationRate = 0.01,
TournamentSize = 10,
TournamentProbability = 0.8,
TargetFitness = 1.0,
Mode = EAMode.MaximizeFitness,
Elites = 1,
CalculateStatistics = true
};
var fitnesslimitCounter = 0;
for (int i = 0; i < 100; i++)
{
var onemaxEA = new OneMaxEA(config, new DefaultRandomNumberGenerator());
onemaxEA.TerminationEvent += (r) =>
{
if (r == TerminationReason.FitnessLimitReached)
{
fitnesslimitCounter++;
}
};
var res = onemaxEA.Evolve();
}
fitnesslimitCounter.Should().BeGreaterOrEqualTo(99);
Console.WriteLine($"Fitness limit reached {fitnesslimitCounter} times");
}
public static void Run()
{
var config = new EAConfiguration
{
PopulationSize = 10,
OverproductionFactor = 1.0,
MaximumGenerations = 100000,
CrossoverType = CrossoverType.OnePoint,
AdultSelectionType = AdultSelectionType.GenerationalReplacement,
ParentSelectionType = ParentSelectionType.Tournament,
CrossoverRate = 0.18,
MutationRate = 0.99,
TournamentSize = 19,
TournamentProbability = 0.77,
TargetFitness = 0.0,
Mode = EAMode.MinimizeFitness,
Elites = 1,
CalculateStatistics = true,
SnapshotFilename = "snapshot2.bin",
SnapshotGenerationInterval = 100
};
var hammingEA = new HammingEA(config, new DefaultRandomNumberGenerator());
var stopwatchtot = new Stopwatch();
var stopwatchgen = new Stopwatch();
PopulationStatistics currentStats = new PopulationStatistics();
hammingEA.PopulationStatisticsCalculated += (stats) =>
{
currentStats = stats;
};
hammingEA.NewBestFitnessEvent += (pheno, gen) => {
double progress = (gen / (double)config.MaximumGenerations) * 100.0;
var totruntime = stopwatchtot.Elapsed;
var genruntime = stopwatchgen.Elapsed;
Console.WriteLine();
Console.WriteLine(string.Format("G# {0} best_f: {1:F2} avg_f: {2:F2} SD: {3:F2} Progress: {4,5:F2} Gen: {5} Tot: {6}", gen, hammingEA.Best.Fitness, currentStats.AverageFitness, currentStats.StandardDeviationFitness, progress, genruntime, totruntime));
Console.WriteLine("Generation winner: " + ((StringGenotype)hammingEA.Best?.Genotype));
stopwatchgen.Restart();
};
hammingEA.TerminationEvent += (r) =>
{
if (r == TerminationReason.FitnessLimitReached)
{
Console.WriteLine($"Fitness limit reached: {hammingEA.Best.Fitness}");
}
};
stopwatchtot.Start();
stopwatchgen.Start();
var res = hammingEA.Evolve();
Console.WriteLine("\n\nDone!");
Console.WriteLine($"Winner: {res.Winner}");
WriteResultToFile(hammingEA.PopulationStatistics);
Console.Read();
}
