public IList <GeneScore> Run(IList <BaseChromosomeType> chromosomeTypes, OptimiserParameters parameters)
{
var allScores = new List <GeneScore>();
var exiting = false;
var iterationCount = 0;
var totalIterations = parameters.MaximumGenerations * parameters.PopulationSize;
var geneValidator = GetGeneValidator(chromosomeTypes, parameters.ParameterConditions);
var evolver = new Evolver(chromosomeTypes, parameters.Seed,
new CompositeGeneValidator(geneValidator, new OnceOnlyGeneValidator()));
evolver.ReportNoProgress += ReportNoProgress;
evolver.ReportNoProgress += delegate(object sender, CancelEventArgs args) { exiting |= args.Cancel; };
_fitnessEvaluator.Initialise(parameters);
IList <Gene> population = evolver.GetInitialPopulation(parameters.PopulationSize);
for (var generation = 0; generation < parameters.MaximumGenerations; generation++)
{
var scoresThisGeneration = new List <GeneScore>();
foreach (var gene in population)
{
var score = _fitnessEvaluator.Evaluate(gene);
scoresThisGeneration.Add(score);
iterationCount++;
if (OnIterationComplete(score, iterationCount, totalIterations - iterationCount))
{
exiting = true;
break;
}
}
scoresThisGeneration.Sort();
if (exiting)
{
break;
}
population = evolver.GetNextGeneration(parameters.ReproductionRate, parameters.MutationRate,
scoresThisGeneration.ConvertAll(score => score.Gene));
allScores.AddRange(scoresThisGeneration);
OnGenerationComplete(generation, scoresThisGeneration);
}
allScores.Sort();
OnComplete();
return(allScores);
}
private void Start()
{
btnStart.Text = "Stop";
tmrRedraw.Enabled = true;
for (int i = Int32.Parse(seedTxtBox.Text); evolvers.Count < EVOLVER_COUNT; i++)
{
Evolver evolver = new Evolver(i, (Pixel[])sourceColours.Clone());
evolvers.Add(evolver);
}
evolvers.ForEach(evolver => evolver.start());
}
private void actionButton_Click(object sender, EventArgs e)
{
if (mode == Mode.Evolve)
{
SetGUI(false);
var cts = new CancellationTokenSource();
// TODO: Handle Cancellation
Evolver <Settings, Genome, Report> .Evolve(
settings, cts,
resultSet =>
{
cohortsBox.Items.Insert(0, resultSet);
cohortsBox.Refresh();
},
errors =>
{
// TODO: Handle Errors Gracefully
throw errors;
},
() =>
{
SetGUI(true);
cohortsBox.SelectedIndex = 0;
mode = Mode.WalkTrail;
actionButton.Text = "Walk Route";
});
}
else
{
antGrid.WalkTrail(SelectedEntity,
Properties.Settings.Default.TrailDelay);
}
}
void NewSimulation(GenerationRecord data)
{
// Increment random seed so that each new run is not identical,
// but still provides determinism, and increased variety.
generationLength = data.GenLength;
InitDroids();
var numWeights = Droids[0].GetNumberOfWeights();
genAlg = new Evolver(
populationSize: Droids.Length,
mutationRate: 0.1f,
crossoverRate: 0.7f,
numWeights: numWeights,
initialGenes: data.Genomes,
elitism: 2,
eliteCopies: 4);
genLengthX = 1f;
generationStart = Time.time;
generationCount = data.GenCount;
SetGenerationText();
DroidEpoch();
}
/// <summary>
/// Runs standard evolution and/or MOEA seeded with maps found by the constrained novelty search.
/// </summary>
/// <param name="maps"> The maps to run evolution on. </param>
/// <param name="r"> The random number generator. </param>
/// <param name="mapSearchOptions"> The map search options. </param>
/// <param name="noveltySearchOptions"> The novelty search options. </param>
/// <param name="mapFitnessOptions"> The map fitness options. </param>
/// <param name="numberOfNoveltyGenerations"> The number of generations to run for the novelty search. </param>
/// <param name="numberOfEvolutionGenerations"> The number of generations to run for the standard evolution. </param>
/// <param name="numberOfMOEAGenerations"> The number of generations to run for the MOEA. </param>
/// <param name="evolutionPopulationSize"> The population size of the standard evolution. </param>
/// <param name="numberOfParents"> The number of parents for the standard evolution. </param>
/// <param name="numberOfChildren"> The number of children spawned per generation in the standard evolution. </param>
/// <param name="evolutionMutationChance"> The chance of mutation happening during evolution. </param>
/// <param name="moeaPopulationSize"> The population size of the MOEA. </param>
/// <param name="moeaMutationChance"> The chance of mutation happening in the MOEA. </param>
/// <param name="selectionStrategy"> The selection strategy used in the standard evolution. </param>
/// <param name="parentSelectionStrategy"> The parent selection strategy used in the standard evolution. </param>
/// <param name="populationStrategy"> The population strategy used in the standard evolution. </param>
/// <param name="folderName"> The folder to save results to in "Images/Finished Maps". </param>
/// <param name="fileToWriteTo"> The file to write timings and fitness per generation to. </param>
/// <param name="selectHighestFitness"> Determines if the maps for seeding are chosen by highest fitness or highest novelty. </param>
/// <param name="lowestFitnessLevelForPrint"> The fitness required before a map is printed. </param>
/// <param name="runEvo"> Determines if the standard evolution should be run. </param>
/// <param name="runMOEA"> Determines if the MOEA should be run. </param>
public static void RunEvolutionWithNoveltyAsBase(
List <MapPhenotype> maps,
Random r,
MapSearchOptions mapSearchOptions = null,
NoveltySearchOptions noveltySearchOptions = null,
MapFitnessOptions mapFitnessOptions = null,
int numberOfNoveltyGenerations = 10,
int numberOfEvolutionGenerations = 10,
int numberOfMOEAGenerations = 10,
int evolutionPopulationSize = 20,
int numberOfParents = 6,
int numberOfChildren = 16,
double evolutionMutationChance = 0.3,
int moeaPopulationSize = 25,
double moeaMutationChance = 0.3,
Enums.SelectionStrategy selectionStrategy = Enums.SelectionStrategy.ChanceBased,
Enums.SelectionStrategy parentSelectionStrategy = Enums.SelectionStrategy.ChanceBased,
Enums.PopulationStrategy populationStrategy = Enums.PopulationStrategy.Mutation,
string folderName = "MapNoveltyEvolution",
string fileToWriteTo = "NoveltyEvolutionHighestFitnessSearchGenerationTimes.txt",
bool selectHighestFitness = true,
double lowestFitnessLevelForPrint = double.MinValue,
bool runEvo = true,
bool runMOEA = true)
{
var stringToWrite = new StringBuilder();
var mso = mapSearchOptions ?? new MapSearchOptions(null);
var mfo = mapFitnessOptions ?? new MapFitnessOptions();
var nso = noveltySearchOptions ?? new NoveltySearchOptions();
var listOfArchives = new List <List <Solution> >();
// Novelty search
var sw = new Stopwatch();
sw.Start();
var baseMapCounter = 0;
foreach (var map in maps)
{
sw.Restart();
stringToWrite.AppendLine(string.Format("Performing novelty search on base map number {0}.", baseMapCounter));
var heightLevels = map.HeightLevels.Clone() as Enums.HeightLevel[, ];
var items = map.MapItems.Clone() as Enums.Item[, ];
var baseMap = new MapPhenotype(heightLevels, items);
baseMap.CreateCompleteMap(Enums.Half.Top, Enums.MapFunction.Turn).SaveMapToPngFile(string.Format("Base Map {0}", baseMapCounter), folderName, false);
mso = new MapSearchOptions(map, mso);
var searcher = new MapSearcher(r, mso, nso);
searcher.RunGenerations(numberOfNoveltyGenerations, stringToWrite);
listOfArchives.Add(searcher.Archive.Archive);
stringToWrite.AppendLine(string.Format("It took {0} ms to perform novelty search on base map number {1}.", sw.ElapsedMilliseconds, baseMapCounter));
stringToWrite.AppendLine();
baseMapCounter++;
}
baseMapCounter = 0;
sw.Restart();
var solutions = new List <List <EvolvableMap> >();
if (selectHighestFitness)
{
stringToWrite.AppendLine(string.Format("Sorting initial population based by highest fitness."));
foreach (var map in maps)
{
var evolvableMaps = new List <EvolvableMap>();
var archive = listOfArchives[baseMapCounter];
foreach (var solution in archive)
{
var ms = (MapSolution)solution;
mso = new MapSearchOptions(map, mso);
var evolvableMap = new EvolvableMap(mso, evolutionMutationChance, r, mfo, ms.MapPoints);
evolvableMap.CalculateFitness();
evolvableMaps.Add(evolvableMap);
}
solutions.Add(evolvableMaps.OrderByDescending(s => s.Fitness).ToList());
baseMapCounter++;
}
stringToWrite.AppendLine(string.Format("It took {0} ms to find maps with the highest fitness.", sw.ElapsedMilliseconds));
sw.Restart();
}
else
{
stringToWrite.AppendLine(string.Format("Sorting initial population based by highest novelty."));
foreach (var map in maps)
{
var evolvableMaps = new List <EvolvableMap>();
var archive = listOfArchives[baseMapCounter];
var tempArchive = archive.OrderByDescending(s => s.Novelty);
foreach (var solution in tempArchive)
{
var ms = (MapSolution)solution;
mso = new MapSearchOptions(map, mso);
var evolvableMap = new EvolvableMap(mso, evolutionMutationChance, r, mfo, ms.MapPoints);
evolvableMap.CalculateFitness();
evolvableMaps.Add(evolvableMap);
}
solutions.Add(evolvableMaps);
baseMapCounter++;
}
stringToWrite.AppendLine(string.Format("It took {0} ms to find maps with the highest novelty.", sw.ElapsedMilliseconds));
sw.Restart();
}
// Evolution
if (runEvo)
{
sw.Restart();
var bestMaps = new List <EvolvableMap>();
baseMapCounter = 0;
foreach (var map in maps)
{
sw.Restart();
stringToWrite.AppendLine(string.Format("Performing evolution on base map number {0}.", baseMapCounter));
mso = new MapSearchOptions(map, mso);
var evolver = new Evolver <EvolvableMap>(
numberOfEvolutionGenerations,
evolutionPopulationSize,
numberOfParents,
numberOfChildren,
evolutionMutationChance,
r,
new object[] { mso, evolutionMutationChance, r, mfo })
{
PopulationSelectionStrategy = selectionStrategy,
ParentSelectionStrategy = parentSelectionStrategy,
PopulationStrategy = populationStrategy
};
evolver.Initialize(solutions[baseMapCounter].Take(evolutionPopulationSize), stringToWrite);
evolver.Evolve(stringToWrite);
var variationValue = 0;
foreach (var individual in evolver.Population)
{
variationValue++;
if (individual.Fitness >= lowestFitnessLevelForPrint)
{
individual.ConvertedPhenotype.SaveMapToPngFile(string.Format("Evo_Base Map {0}_Map {1}_Fitness {2}", baseMapCounter, variationValue, individual.Fitness), folderName, false);
}
}
stringToWrite.AppendLine(string.Format("It took {0} ms to perform evolution on base map number {1}.", sw.ElapsedMilliseconds, baseMapCounter));
stringToWrite.AppendLine();
bestMaps.Add(evolver.Population.OrderByDescending(evoMap => evoMap.Fitness).ToList()[0]);
MapHelper.SaveGreyscaleNoveltyMap(evolver.Population, string.Format("Evo_Base Map {0} NoveltyMap", baseMapCounter), folderName);
baseMapCounter++;
}
MapHelper.SaveGreyscaleNoveltyMap(bestMaps, string.Format("Evo_Best Maps NoveltyMap"), folderName);
}
// MOEA
if (runMOEA)
{
baseMapCounter = 0;
var bestMaps = new List <EvolvableMap>();
foreach (var map in maps)
{
sw.Restart();
stringToWrite.AppendLine(string.Format("Performing multi objective evolution for base map number {0}", baseMapCounter));
var heightLevels = map.HeightLevels.Clone() as Enums.HeightLevel[, ];
var items = map.MapItems.Clone() as Enums.Item[, ];
var baseMap = new MapPhenotype(heightLevels, items);
baseMap.CreateCompleteMap(Enums.Half.Top, Enums.MapFunction.Mirror);
baseMap.SaveMapToPngFile(string.Format("Base Map {0}", baseMapCounter), folderName, false);
mso = new MapSearchOptions(map, mso);
var evolver = new MultiObjectiveEvolver(
numberOfMOEAGenerations,
moeaPopulationSize,
moeaMutationChance,
r,
mso,
mfo);
evolver.RunEvolution(stringToWrite, solutions[baseMapCounter].Take(moeaPopulationSize));
var variationValue = 0;
foreach (var individual in evolver.Population)
{
variationValue++;
if (individual.Fitness >= lowestFitnessLevelForPrint)
{
individual.ConvertedPhenotype.SaveMapToPngFile(string.Format("MOEA_Base Map {0}_Map {1}_Fitness {2}", baseMapCounter, variationValue, individual.Fitness), folderName, false);
}
}
stringToWrite.AppendLine(string.Format("It took {0} ms to perform multi objective evolution on base map number {1}", sw.ElapsedMilliseconds, baseMapCounter));
stringToWrite.AppendLine();
bestMaps.Add(evolver.Population.OrderByDescending(evoMap => evoMap.Fitness).ToList()[0]);
MapHelper.SaveGreyscaleNoveltyMap(evolver.Population, string.Format("MOEA_Base Map {0} NoveltyMap", baseMapCounter), folderName);
baseMapCounter++;
}
MapHelper.SaveGreyscaleNoveltyMap(bestMaps, string.Format("MOEA_Best Maps NoveltyMap"), folderName);
}
WriteToTextFile(stringToWrite.ToString(), fileToWriteTo, folderName);
}
/// <summary>
/// Runs evolution with the given settings.
/// </summary>
/// <param name="maps"> The maps to run evolution on. </param>
/// <param name="r"> The random number generator. </param>
/// <param name="mapSearchOptions"> The map search options. </param>
/// <param name="mapFitnessOptions"> The map fitness options. </param>
/// <param name="numberOfGenerations"> The number of generations to run. </param>
/// <param name="populationSize"> The population size of the evolution. </param>
/// <param name="numberOfParents"> The number of parents used in the evolution. </param>
/// <param name="numberOfChildren"> The number of children spawned per generation. </param>
/// <param name="mutationChance"> The chance of mutation happening. </param>
/// <param name="selectionStrategy"> The selection strategy. </param>
/// <param name="parentSelectionStrategy"> The parent selection strategy. </param>
/// <param name="populationStrategy"> The population strategy. </param>
/// <param name="folderName"> The folder to save results to in "Images/Finished Maps". </param>
/// <param name="fileToWriteTo"> The file to write timings and fitness per generation to. </param>
/// <param name="lowestFitnessLevelForPrint"> The fitness required before a map is printed. </param>
public static void RunEvolution(
List <MapPhenotype> maps,
Random r,
MapSearchOptions mapSearchOptions = null,
MapFitnessOptions mapFitnessOptions = null,
int numberOfGenerations = 10,
int populationSize = 20,
int numberOfParents = 6,
int numberOfChildren = 16,
double mutationChance = 0.3,
Enums.SelectionStrategy selectionStrategy = Enums.SelectionStrategy.ChanceBased,
Enums.SelectionStrategy parentSelectionStrategy = Enums.SelectionStrategy.ChanceBased,
Enums.PopulationStrategy populationStrategy = Enums.PopulationStrategy.Mutation,
string folderName = "MapEvolution",
string fileToWriteTo = "EvolutionGenerationTimes.txt",
double lowestFitnessLevelForPrint = double.MinValue)
{
var stringToWrite = new StringBuilder();
var mso = mapSearchOptions ?? new MapSearchOptions(null);
var mfo = mapFitnessOptions ?? new MapFitnessOptions();
var bestMaps = new List <EvolvableMap>();
var sw = new Stopwatch();
sw.Start();
var baseMapCounter = 0;
foreach (var map in maps)
{
sw.Restart();
stringToWrite.AppendLine(string.Format("Performing evolution on base map number {0}.", baseMapCounter));
var heightLevels = map.HeightLevels.Clone() as Enums.HeightLevel[, ];
var items = map.MapItems.Clone() as Enums.Item[, ];
var baseMap = new MapPhenotype(heightLevels, items);
baseMap.CreateCompleteMap(Enums.Half.Top, Enums.MapFunction.Turn).SaveMapToPngFile(string.Format("Base Map {0}", baseMapCounter), folderName, false);
mso = new MapSearchOptions(map, mso);
var evolver = new Evolver <EvolvableMap>(
numberOfGenerations,
populationSize,
numberOfParents,
numberOfChildren,
mutationChance,
r,
new object[] { mso, mutationChance, r, mfo })
{
PopulationSelectionStrategy = selectionStrategy,
ParentSelectionStrategy = parentSelectionStrategy,
PopulationStrategy = populationStrategy
};
evolver.Initialize(stringToWrite);
var bestMap = evolver.Evolve(stringToWrite);
var variationValue = 0;
foreach (var individual in evolver.Population)
{
variationValue++;
if (individual.Fitness >= lowestFitnessLevelForPrint)
{
individual.ConvertedPhenotype.SaveMapToPngFile(string.Format("Base Map {0}_Map {1}_Fitness {2}", baseMapCounter, variationValue, individual.Fitness), folderName, false);
}
}
stringToWrite.AppendLine(string.Format("It took {0} ms to perform evolution on base map number {1}.", sw.ElapsedMilliseconds, baseMapCounter));
stringToWrite.AppendLine();
bestMaps.Add(evolver.Population.OrderByDescending(evoMap => evoMap.Fitness).ToList()[0]);
MapHelper.SaveGreyscaleNoveltyMap(evolver.Population, string.Format("Base Map {0} NoveltyMap", baseMapCounter), folderName);
Console.WriteLine("The map fitness values of base map {0} were: {1}", baseMapCounter, ((EvolvableMap)bestMap).MapFitnessValues);
baseMapCounter++;
}
MapHelper.SaveGreyscaleNoveltyMap(bestMaps, string.Format(" Best Maps NoveltyMap"), folderName);
WriteToTextFile(stringToWrite.ToString(), fileToWriteTo, folderName);
}
private void Start()
{
btnStart.Text = "Stop";
tmrRedraw.Enabled = true;
for (int i = Int32.Parse(seedTxtBox.Text); evolvers.Count < EVOLVER_COUNT; i++)
{
Evolver evolver = new Evolver(i, (Pixel[])sourceColours.Clone());
evolvers.Add(evolver);
}
evolvers.ForEach(evolver => evolver.start());
}
public GeneticTrainer(Evolver <EvolvableNet> evolver, GeneticTrainerOptions options) : base(options, new GeneticSession(null))
{
_evolver = evolver;
}
//Class for miscellaneous test functions.
public static void TestFullMultiGeneration()
{
//Should probably generalize this to a "tag case" object...
double dx = 1.1d;
StopWatch tagload = new StopWatch("tag load");
tagload.tic();
Tag[] tags = Tag.ExtractFromFile("geom-sets/cylinder-hollow/tags.tg");
tagload.toc();
Console.WriteLine("Loading tags...");
Console.WriteLine(tagload.Result());
string[] filenames = new string[tags.Length];
int k = 0;
foreach (Tag t in tags)
{
Console.WriteLine(t.ToString());
filenames[k] = t.RegionFilename;
Console.WriteLine(filenames[k] + "?exist=" + File.Exists(filenames[k]).ToString());
k++;
}
Console.WriteLine();
Console.WriteLine("Generating points...");
StopWatch buildpoints = new StopWatch("build points");
buildpoints.tic();
double[] bounds = PointCloud.GetBoundsFromStls(filenames);
PointCloud pc = PointCloud.FromBounds(bounds, dx);
pc.WriteToCsv("./testcsv/diag.csv");
buildpoints.toc();
Console.WriteLine(buildpoints.Result());
Console.WriteLine();
Console.WriteLine("Initializing lattice...");
StopWatch initlat = new StopWatch("initialize lattice");
initlat.tic();
LatticeStateInitializer init = new LatticeStateInitializer(pc, tags, 4, 20, 1.01 * dx);
init.InitializeNodes();
initlat.toc();
Console.WriteLine(initlat.Result());
Console.WriteLine();
Console.WriteLine("Building lattice...");
StopWatch blat = new StopWatch("build lattice");
blat.tic();
LatticeState s = init.BuildLatticeState();
blat.toc();
Console.WriteLine(blat.Result());
Console.WriteLine();
StopWatch file = new StopWatch("write to file");
Console.WriteLine("Writing to directory...");
file.tic();
s.WriteToDirectory("./lattice-output-test/cylgen", true);
file.toc();
Console.WriteLine(file.Result());
Console.WriteLine();
Console.WriteLine("Done.");
Console.WriteLine();
Console.WriteLine("Evolving...");
Evolver e = new Evolver(s, 0.1, 4);
e.EvolveAll("evolvetest", 500, true);
}
private void tmrRedraw_Tick(object sender, EventArgs e)
{
// select current;
Evolver evolver = currentEvolver;
if (evolver == null)
{
Console.WriteLine("evolver fail");
return;
}
lock (evolver.evolverLock)
{
currentDrawing = evolver.currentDrawing.Clone();
}
if (currentDrawing == null)
{
Console.WriteLine("currentDrawing fail");
return;
}
String txt = String.Format("Current : {0}\r\n", currIndex);
for (int i = 0; i < EVOLVER_COUNT; i++)
{
txt += String.Format("Evolver {0} : {1}\r\n", i, evolvers[i].errorLevel);
}
textBox1.Text = txt;
toolStripStatusLabelFitness.Text = evolver.errorLevel.ToString();
toolStripStatusLabelGeneration.Text = evolver.generation.ToString();
toolStripStatusLabelSelected.Text = evolver.selected.ToString();
toolStripStatusLabelPolygons.Text = evolver.currentDrawing.Shapes.Count.ToString();
if (EVOLVER_COUNT > 1)
{
Evolver e1 = evolvers.Find(ev => ev.readyToBreed);
if (e1 != null)
{
Evolver e2 = evolvers.Find(ev => ev.readyToBreed && ev != e1);
if (e2 != null)
{
e1.breed(e2);
}
}
}
bool shouldRepaint = false;
if (repaintIntervall.Ticks > 0)
{
if (lastRepaint < DateTime.Now - repaintIntervall)
{
shouldRepaint = true;
}
}
if (repaintOnSelectedSteps > 0)
{
if (lastSelected + repaintOnSelectedSteps < evolver.selected)
{
shouldRepaint = true;
}
}
if (shouldRepaint)
{
redraw();
}
}
