public EvolutionAlgorithm initializeEvolutionAlgorithm(IPopulationEvaluator popEval, int popSize, AssessGenotypeFunction assess, List <long> parentGenomeIDs = null)
{
//have to add our seed to the parents!
if (officialSeedGenome != null)
{
//if we aren't given any parents, make a new list, and add the seed
if (parentGenomeIDs == null)
{
parentGenomeIDs = new List <long>();
}
parentGenomeIDs.Add(officialSeedGenome.GenomeId);
}
//create our initial population, using seeds or not, making sure it is at least "popsize" long
GenomeList gl = createGenomeList(popSize, assess, parentGenomeIDs);
//now we have a genomelist full of our parents, if they didn't die in a car crash when we were young, yay!
//also, these parents, their connections, and neurons are safely catalogued by WIN (eventually...)
Population pop = new Population(idgen, gl);
//create our algorithm
evoAlgorithm = new EvolutionAlgorithm(pop, popEval, neatParams, assess);
createExperimentDirectory();
//send it away!
return(evoAlgorithm);
}
public void initalizeEvoManager(int cInputs, int cOuputs, NeatParameters np, IPopulationEvaluator popEval)
{
cppnInputs = cInputs;
cppnOutputs = cOuputs;
neatParams = np;
populationEval = popEval;
}
public void ResetEvaluator(IActivationFunction activationFn)
{
m_eventAllAgentsRegistered.WaitOne();
populationEvaluator = new
EveryonePopulationEvaluator(new FourDFieldSubsNetworkEvaluator(m_neatQTables));
}
public void ResetEvaluator(IActivationFunction activationFn)
{
if (multiple)
{
populationEvaluator = new SkirmishPopulationEvaluator(new SkirmishNetworkEvaluator(5, shape));
}
else
{
populationEvaluator = new SkirmishPopulationEvaluator(new SkirmishNetworkEvaluator(1, shape));
}
}
public NoveltyThread(JSPopulationEvaluator jsPop, AssessGenotypeFunction assess, int popSize)
{
//save our objects for executing later!
popEval = jsPop;
populationSize = popSize;
autoEvent = new AutoResetEvent(false);
waitNextTime = true;
novelThread = new Thread(delegate()
{
autoEvent.WaitOne();
//we'll start by testing with 0 parents, and popsize of 15 yay!
noveltyRun = EvolutionManager.SharedEvolutionManager.initializeEvolutionAlgorithm(popEval, populationSize, assess);
//let our algoirhtm know we want to do novelty gosh darnit
if (noveltyRun.multiobjective != null)
{
noveltyRun.multiobjective.doNovelty = true;
}
//we make sure we don't wait in this loop, since we just got started!
waitNextTime = false;
while (true)
{
//this will cause us to pause!
if (waitNextTime)
{
waitNextTime = false;
autoEvent.WaitOne();
}
// Start the stopwatch we'll use to measure eval performance
Stopwatch sw = Stopwatch.StartNew();
//run the generation
noveltyRun.PerformOneGeneration();
// Stop the stopwatch
sw.Stop();
// Report the results
Console.WriteLine("Time used per gen (float): {0} ms", sw.Elapsed.TotalMilliseconds);
Console.WriteLine("Time used per gen (rounded): {0} ms", sw.ElapsedMilliseconds);
}
});
novelThread.Start();
}
/// <summary>
/// Default Constructor.
/// </summary>
public EvolutionAlgorithm(Population pop, IPopulationEvaluator populationEvaluator, NeatParameters neatParameters)
{
this.pop = pop;
this.populationEvaluator = populationEvaluator;
this.neatParameters = neatParameters;
neatParameters_Normal = neatParameters;
neatParameters_PrunePhase = new NeatParameters(neatParameters);
neatParameters_PrunePhase.pMutateAddConnection = 0.0;
neatParameters_PrunePhase.pMutateAddNode = 0.0;
neatParameters_PrunePhase.pMutateAddModule = 0.0;
neatParameters_PrunePhase.pMutateConnectionWeights = 0.33;
neatParameters_PrunePhase.pMutateDeleteConnection = 0.33;
neatParameters_PrunePhase.pMutateDeleteSimpleNeuron = 0.33;
// Disable all crossover as this has a tendency to increase complexity, which is precisely what
// we don't want during a pruning phase.
neatParameters_PrunePhase.pOffspringAsexual = 1.0;
neatParameters_PrunePhase.pOffspringSexual = 0.0;
if(neatParameters.multiobjective) {
this.multiobjective=new Multiobjective.Multiobjective(neatParameters);
neatParameters.compatibilityThreshold=100000000.0; //disable speciation w/ multiobjective
}
if(neatParameters.noveltySearch)
{
if(neatParameters.noveltyHistogram)
{
this.noveltyFixed = new noveltyfixed(neatParameters.archiveThreshold);
this.histogram = new noveltyhistogram(neatParameters.histogramBins);
noveltyInitialized=true;
InitialisePopulation();
}
if(neatParameters.noveltyFixed || neatParameters.noveltyFloat)
{
this.noveltyFixed = new noveltyfixed(neatParameters.archiveThreshold);
InitialisePopulation();
noveltyFixed.initialize(this.pop);
noveltyInitialized=true;
populationEvaluator.EvaluatePopulation(pop, this);
UpdateFitnessStats();
DetermineSpeciesTargetSize();
}
}
else
{
InitialisePopulation();
}
}
public NoveltyThread(JSPopulationEvaluator jsPop, AssessGenotypeFunction assess, int popSize)
{
//save our objects for executing later!
popEval = jsPop;
populationSize = popSize;
autoEvent = new AutoResetEvent(false);
waitNextTime = true;
novelThread = new Thread(delegate()
{
autoEvent.WaitOne();
//we'll start by testing with 0 parents, and popsize of 15 yay!
noveltyRun = EvolutionManager.SharedEvolutionManager.initializeEvolutionAlgorithm(popEval, populationSize, assess);
//let our algoirhtm know we want to do novelty gosh darnit
if(noveltyRun.multiobjective != null)
noveltyRun.multiobjective.doNovelty = true;
//we make sure we don't wait in this loop, since we just got started!
waitNextTime = false;
while (true)
{
//this will cause us to pause!
if (waitNextTime)
{
waitNextTime = false;
autoEvent.WaitOne();
}
// Start the stopwatch we'll use to measure eval performance
Stopwatch sw = Stopwatch.StartNew();
//run the generation
noveltyRun.PerformOneGeneration();
// Stop the stopwatch
sw.Stop();
// Report the results
Console.WriteLine("Time used per gen (float): {0} ms", sw.Elapsed.TotalMilliseconds);
Console.WriteLine("Time used per gen (rounded): {0} ms", sw.ElapsedMilliseconds);
}
});
novelThread.Start();
}
/// <summary>
/// Default Constructor.
/// </summary>
public EvolutionAlgorithm(Population pop, IPopulationEvaluator populationEvaluator) : this(pop, populationEvaluator, new NeatParameters())
{}
public void ResetEvaluator(SharpNeatLib.NeuralNetwork.IActivationFunction activationFn)
{
//populationEval = new SingleFilePopulationEvaluator(new NetworkEvaluator(simExp),null);
populationEval = new MultiThreadedPopulationEvaluator(new NetworkEvaluator(simExp),null);
}
public void ResetEvaluator(IActivationFunction activationFn)
{
// populationEvaluator = new SingleFilePopulationEvaluator(new RobotDualNetworkEvaluator(), activationFn);
populationEvaluator = new PacmanPopulationEvaluatorSUPG(new PacmanNetworkEvaluatorSUPG(), activationFn);
}
public EvolutionAlgorithm initializeEvolutionAlgorithm(IPopulationEvaluator popEval, int popSize, AssessGenotypeFunction assess, List<long> parentGenomeIDs = null)
{
//have to add our seed to the parents!
if (officialSeedGenome != null)
{
//if we aren't given any parents, make a new list, and add the seed
if (parentGenomeIDs == null)
parentGenomeIDs = new List<long>();
parentGenomeIDs.Add(officialSeedGenome.GenomeId);
}
//create our initial population, using seeds or not, making sure it is at least "popsize" long
GenomeList gl = createGenomeList(popSize, assess, parentGenomeIDs);
//now we have a genomelist full of our parents, if they didn't die in a car crash when we were young, yay!
//also, these parents, their connections, and neurons are safely catalogued by WIN (eventually...)
Population pop = new Population(idgen, gl);
//create our algorithm
evoAlgorithm = new EvolutionAlgorithm(pop, popEval, neatParams, assess);
createExperimentDirectory();
//send it away!
return evoAlgorithm;
}
public void ResetEvaluator(IActivationFunction activationFn)
{
populationEvaluator = new
EveryonePopulationEvaluator(new CCEAGeomCtrlNetworkEvaluator(m_neatQTable));
}
public void ResetEvaluator(IActivationFunction activationFn)
{
populationEvaluator = new
EveryonePopulationEvaluator(new FieldSubs3LNetworkEvaluator(m_neatQTable));
}
public void ResetEvaluator(IActivationFunction activationFn)
{
populationEvaluator = new SingleFilePopulationEvaluator(new XorNetworkEvaluator(), activationFn);
}
public void ResetEvaluator(SharpNeatLib.NeuralNetwork.IActivationFunction activationFn)
{
//populationEval = new SingleFilePopulationEvaluator(new NetworkEvaluator(simExp),null);
populationEval = new MultiThreadedPopulationEvaluator(new NetworkEvaluator(simExp), null);
}
public void ResetEvaluator(IActivationFunction activationFn)
{
populationEvaluator = new EveryonePopulationEvaluator(new
Skirmish3DNetworkEvaluator(m_numAgents, shape));
}
public void ResetEvaluator(IActivationFunction activationFn)
{
if(multiple)
populationEvaluator = new SkirmishPopulationEvaluator(new SkirmishNetworkEvaluator(5, shape));
else
populationEvaluator = new SkirmishPopulationEvaluator(new SkirmishNetworkEvaluator(1, shape));
}
/// <summary>
/// Default Constructor.
/// </summary>
public EvolutionAlgorithm(Population pop, IPopulationEvaluator populationEvaluator, NeatParameters neatParameters)
{
this.pop = pop;
this.populationEvaluator = populationEvaluator;
this.neatParameters = neatParameters;
neatParameters_Normal = neatParameters;
neatParameters_PrunePhase = new NeatParameters(neatParameters);
neatParameters_PrunePhase.pMutateAddConnection = 0.0;
neatParameters_PrunePhase.pMutateAddNode = 0.0;
neatParameters_PrunePhase.pMutateConnectionWeights = 0.33;
neatParameters_PrunePhase.pMutateDeleteConnection = 0.33;
neatParameters_PrunePhase.pMutateDeleteSimpleNeuron = 0.33;
// Disable all crossover as this has a tendency to increase complexity, which is precisely what
// we don't want during a pruning phase.
neatParameters_PrunePhase.pOffspringAsexual = 1.0;
neatParameters_PrunePhase.pOffspringSexual = 0.0;
InitialisePopulation();
}
/// <summary>
/// Default Constructor.
/// </summary>
public EvolutionAlgorithm(Population pop, IPopulationEvaluator populationEvaluator, NeatParameters neatParameters)
{
this.pop = pop;
this.populationEvaluator = populationEvaluator;
this.neatParameters = neatParameters;
neatParameters_Normal = neatParameters;
neatParameters_PrunePhase = new NeatParameters(neatParameters);
neatParameters_PrunePhase.pMutateAddConnection = 0.0;
neatParameters_PrunePhase.pMutateAddNode = 0.0;
neatParameters_PrunePhase.pMutateAddModule = 0.0;
neatParameters_PrunePhase.pMutateConnectionWeights = 0.33;
neatParameters_PrunePhase.pMutateDeleteConnection = 0.33;
neatParameters_PrunePhase.pMutateDeleteSimpleNeuron = 0.33;
// Disable all crossover as this has a tendency to increase complexity, which is precisely what
// we don't want during a pruning phase.
neatParameters_PrunePhase.pOffspringAsexual = 1.0;
neatParameters_PrunePhase.pOffspringSexual = 0.0;
if (neatParameters.mapelites)
{
meInitialisePopulation();
meGridInit(pop);
Console.WriteLine("Mapelites stuff has been initialized. Oh btw, we're doing mapelites.");
if (neatParameters.me_simpleGeneticDiversity)
{
Console.WriteLine("Mapelites reinforced by the power of 51MPLE gENET1C d1VER51TY!!!!1 *fireworks* *applause* *receive phd*");
}
if (neatParameters.me_noveltyPressure)
{
Console.WriteLine("Mapelites now with NOVELTY PRESSURE! (>'')>");
}
} // Skip all that other stupid shit if we are doing MapElites
else if (neatParameters.NS2)
{
if (neatParameters.NS1) ns1 = true;
ns2InitializePopulation();
if (neatParameters.track_me_grid)
{
Console.WriteLine("Initializing mapelites-style-grid genome tracking..");
meGridInit(pop);
}
Console.WriteLine("Novelty Search 2.0 has been initialized.");
} // Skip the code jungle below if we are doing Novelty Search 2.0
else if (neatParameters.NSLC) // (Steady-State NSLC -- NEW!!)
{
// TODO: JUSTIN: SS-NSLC GOES HERE!
ns1 = true;
ns2InitializePopulation();
if (neatParameters.track_me_grid)
{
Console.WriteLine("Initializing mapelites-style-grid genome tracking..");
meGridInit(pop);
}
Console.WriteLine("Initializing STEADY STATE -- NSLC! NEW! This is a thing that is happening now. You cannot stop it. Relax.");
// TODO: INITIALIZATION for SS-NSLC (is like NS1... but make it separate so we can stop being so intertwined. cleaner is better, yo)
} // Skip the nasty quagmire of unverified bogus rotten banana sandwiches if doing Steady-State NSLC
else
{
if (neatParameters.multiobjective)
{
this.multiobjective = new Multiobjective.Multiobjective(neatParameters);
neatParameters.compatibilityThreshold = 100000000.0; //disable speciation w/ multiobjective
}
if (neatParameters.noveltySearch)
{
if (neatParameters.noveltyHistogram)
{
this.noveltyFixed = new noveltyfixed(neatParameters.archiveThreshold);
this.histogram = new noveltyhistogram(neatParameters.histogramBins);
noveltyInitialized = true;
InitialisePopulation();
}
if (neatParameters.noveltyFixed || neatParameters.noveltyFloat)
{
this.noveltyFixed = new noveltyfixed(neatParameters.archiveThreshold);
InitialisePopulation();
noveltyFixed.initialize(this.pop);
noveltyInitialized = true;
populationEvaluator.EvaluatePopulation(pop, this);
UpdateFitnessStats();
DetermineSpeciesTargetSize();
}
if (neatParameters.track_me_grid)
{
Console.WriteLine("Initializing mapelites-style-grid genome tracking..");
meGridInit(pop); // JUSTIN: Trying to add grid-tracking to NS1
}
}
else
{
InitialisePopulation();
if (neatParameters.track_me_grid)
{
Console.WriteLine("Initializing mapelites-style-grid genome tracking..");
meGridInit(pop); // JUSTIN: Trying to add grid-tracking to fitness-based search
}
}
}
}
public void ResetEvaluator(IActivationFunction activationFn)
{
populationEvaluator = new
EveryonePopulationEvaluator(new HyperNEATPlayerNetworkEvaluator(m_neatQTable));
}
public void initalizeEvoManager(int cInputs, int cOuputs, NeatParameters np, IPopulationEvaluator popEval)
{
cppnInputs = cInputs;
cppnOutputs = cOuputs;
neatParams = np;
populationEval = popEval;
}
public void ResetEvaluator(IActivationFunction activationFn)
{
populationEvaluator = new ClunePopulationEvaluator(new CluneNetworkEvaluator(inputs, outputs, hidden));
}
public void ResetEvaluator(IActivationFunction activationFn)
{
// populationEvaluator = new SingleFilePopulationEvaluator(new RobotDualNetworkEvaluator(), activationFn);
populationEvaluator = new FoodGathererPopulationEvaluator(new FoodGathererNetworkEvaluator(), activationFn);
}
public void ResetEvaluator(IActivationFunction activationFn)
{
populationEvaluator = new EveryonePopulationEvaluator(
new SkirmishCCEANetworkEvaluator(m_agentId));
//populationEvaluator = new SkirmishPopulationEvaluator(new SkirmishNetworkEvaluator(1, shape));
}
