/// <summary>
/// Create and return a SteadyStateNeatEvolutionAlgorithm object (specific to fitness-based evaluations) ready for
/// running the
/// NEAT algorithm/search based on the given genome factory and genome list. Various sub-parts of the algorithm are
/// also constructed and connected up.
/// </summary>
/// <param name="genomeFactory">The genome factory from which to generate new genomes</param>
/// <param name="genomeList">The current genome population</param>
/// <param name="startingEvaluations">The number of evaluations that have been executed prior to the current run.</param>
/// <returns>Constructed evolutionary algorithm</returns>
public override INeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(
IGenomeFactory<NeatGenome> genomeFactory,
List<NeatGenome> genomeList, ulong startingEvaluations)
{
ulong initializationEvaluations;
// Instantiate the internal initialization algorithm
_mazeNavigationInitializer.InitializeAlgorithm(ParallelOptions, genomeList,
CreateGenomeDecoder(), startingEvaluations);
// Run the initialization algorithm until the requested number of viable seed genomes are found
List<NeatGenome> seedPopulation = _mazeNavigationInitializer.EvolveViableGenomes(SeedGenomeCount, true,
out initializationEvaluations);
// Create complexity regulation strategy.
var complexityRegulationStrategy =
ExperimentUtils.CreateComplexityRegulationStrategy(ComplexityRegulationStrategy, Complexitythreshold);
// Create the evolution algorithm.
AbstractNeatEvolutionAlgorithm<NeatGenome> ea =
new QueueingNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
new ParallelKMeansClusteringStrategy<NeatGenome>(new ManhattanDistanceMetric(1.0, 0.0, 10.0),
ParallelOptions),
complexityRegulationStrategy, _batchSize, RunPhase.Primary, (_bridgingMagnitude > 0),
false, _evolutionDataLogger, _experimentLogFieldEnableMap);
// Create IBlackBox evaluator.
IPhenomeEvaluator<IBlackBox, BehaviorInfo> mazeNavigationEvaluator =
new MazeNavigationMCSEvaluator(MaxDistanceToTarget, MaxTimesteps,
MazeVariant, MinSuccessDistance, _behaviorCharacterizationFactory,
_bridgingMagnitude, _bridgingApplications, initializationEvaluations);
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// IGenomeEvaluator<NeatGenome> fitnessEvaluator =
// new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
// SelectionType.Queueing, SearchType.MinimalCriteriaSearch, _evaluationDataLogger,
// SerializeGenomeToXml);
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
SelectionType.Queueing, SearchType.MinimalCriteriaSearch, _evaluationDataLogger,
SerializeGenomeToXml);
// Initialize the evolution algorithm.
ea.Initialize(fitnessEvaluator, genomeFactory, seedPopulation, DefaultPopulationSize,
null, MaxEvaluations + startingEvaluations);
// Finished. Return the evolution algorithm
return ea;
}
/// <summary>
/// Create and return a SteadyStateNeatEvolutionAlgorithm object (specific to fitness-based evaluations) ready for
/// running the
/// NEAT algorithm/search based on the given genome factory and genome list. Various sub-parts of the algorithm are
/// also constructed and connected up.
/// </summary>
/// <param name="genomeFactory">The genome factory from which to generate new genomes</param>
/// <param name="genomeList">The current genome population</param>
/// <param name="startingEvaluations">The number of evaluations that have been executed prior to the current run.</param>
/// <returns>Constructed evolutionary algorithm</returns>
public override INeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(
IGenomeFactory<NeatGenome> genomeFactory,
List<NeatGenome> genomeList, ulong startingEvaluations)
{
// Create complexity regulation strategy.
var complexityRegulationStrategy =
ExperimentUtils.CreateComplexityRegulationStrategy(ComplexityRegulationStrategy, Complexitythreshold);
// Create the evolution algorithm.
AbstractNeatEvolutionAlgorithm<NeatGenome> ea =
new QueueingNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
new ParallelKMeansClusteringStrategy<NeatGenome>(new ManhattanDistanceMetric(1.0, 0.0, 10.0),
ParallelOptions),
complexityRegulationStrategy, _batchSize, RunPhase.Primary);
// Create IBlackBox evaluator.
IPhenomeEvaluator<IBlackBox, FitnessInfo> mazeNavigationEvaluator =
new MazeNavigationRandomEvaluator(MaxDistanceToTarget, MaxTimesteps,
MazeVariant, MinSuccessDistance);
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new SerialGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
_evaluationDataLogger);
// IGenomeEvaluator<NeatGenome> fitnessEvaluator =
// new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
// _evaluationDataLogger, SerializeGenomeToXml);
// Initialize the evolution algorithm.
ea.Initialize(fitnessEvaluator, genomeFactory, genomeList, DefaultPopulationSize,
null, MaxEvaluations);
// Finished. Return the evolution algorithm
return ea;
}
/// <summary>
/// Creates the evolution algorithm container using the given factories and genome lists.
/// </summary>
/// <param name="genomeFactory1">The agent genome factory.</param>
/// <param name="genomeFactory2">The maze genome factory.</param>
/// <param name="genomeList1">The agent genome list.</param>
/// <param name="genomeList2">The maze genome list.</param>
/// <returns>The instantiated coevolution algorithm container.</returns>
public override ICoevolutionAlgorithmContainer<NeatGenome, MazeGenome> CreateCoevolutionAlgorithmContainer(
IGenomeFactory<NeatGenome> genomeFactory1, IGenomeFactory<MazeGenome> genomeFactory2,
List<NeatGenome> genomeList1, List<MazeGenome> genomeList2)
{
List<NeatGenome> seedAgentPopulation = new List<NeatGenome>();
// Compute the maze max complexity
((MazeGenomeFactory) genomeFactory2).MaxComplexity = MazeUtils.DetermineMaxPartitions(_mazeHeight,
_mazeWidth, 200);
// Create maze decoder to decode initialization mazes
MazeDecoder mazeDecoder = new MazeDecoder(_mazeHeight, _mazeWidth, _mazeScaleMultiplier);
// Loop through every maze and evolve the requisite number of viable genomes that solve it
for (int idx = 0; idx < genomeList2.Count; idx++)
{
Console.WriteLine(@"Evolving viable agents for maze population index {0} and maze ID {1}", idx,
genomeList2[idx].Id);
// Evolve the number of agents required to meet the success MC for the current maze
List<NeatGenome> viableMazeAgents = EvolveViableAgents(genomeFactory1, genomeList1.ToList(),
mazeDecoder.Decode(genomeList2[idx]));
// Add the viable agent genomes who solve the current maze (but avoid adding duplicates, as identified by the genome ID)
// Note that it's fine to have multiple mazes solved by the same agent, so in this case, we'll leave the agent
// in the pool of seed agent genomes
foreach (
NeatGenome viableMazeAgent in
viableMazeAgents.Where(
viableMazeAgent =>
seedAgentPopulation.Select(sap => sap.Id).Contains(viableMazeAgent.Id) == false))
{
seedAgentPopulation.Add(viableMazeAgent);
}
}
// If we still lack the genomes to fill out the agent seed genome count while still satisfying the maze MC,
// iteratively pick a random maze and evolve agents on that maze until we reach the requisite number
while (seedAgentPopulation.ToList().Count < AgentSeedGenomeCount)
{
FastRandom rndMazePicker = new FastRandom();
// Pick a random maze on which to evolve agent(s)
MazeGenome mazeGenome = genomeList2[rndMazePicker.Next(genomeList2.Count - 1)];
Console.WriteLine(
@"Continuing viable agent evolution on maze {0}, with {1} of {2} required agents in place",
mazeGenome.Id, seedAgentPopulation.Count, AgentSeedGenomeCount);
// Evolve the number of agents required to meet the success MC for the maze
List<NeatGenome> viableMazeAgents = EvolveViableAgents(genomeFactory1, genomeList1.ToList(),
mazeDecoder.Decode(mazeGenome));
// Iterate through each viable agent and remove them if they've already solved a maze or add them to the list
// of viable agents if they have not
foreach (NeatGenome viableMazeAgent in viableMazeAgents)
{
// If they agent has already solved maze and is in the list of viable agents, remove that agent
// from the pool of seed genomes (this is done because here, we're interested in getting unique
// agents and want to avoid an endless loop wherein the same viable agents are returned)
if (seedAgentPopulation.Select(sap => sap.Id).Contains(viableMazeAgent.Id))
{
genomeList1.Remove(viableMazeAgent);
}
// Otherwise, add that agent to the list of viable agents
else
{
seedAgentPopulation.Add(viableMazeAgent);
}
}
}
// Set dummy fitness so that seed maze(s) will be marked as evaluated
foreach (MazeGenome mazeGenome in genomeList2)
{
mazeGenome.EvaluationInfo.SetFitness(0);
}
// Create the NEAT (i.e. navigator) queueing evolution algorithm
AbstractEvolutionAlgorithm<NeatGenome> neatEvolutionAlgorithm =
new QueueingNeatEvolutionAlgorithm<NeatGenome>(
new NeatEvolutionAlgorithmParameters
{
SpecieCount = AgentNumSpecies,
MaxSpecieSize =
AgentNumSpecies > 0
? AgentDefaultPopulationSize/AgentNumSpecies
: AgentDefaultPopulationSize
},
new ParallelKMeansClusteringStrategy<NeatGenome>(new ManhattanDistanceMetric(1.0, 0.0, 10.0),
ParallelOptions), null,
NavigatorBatchSize, RunPhase.Primary, false, false, _navigatorEvolutionDataLogger,
_navigatorLogFieldEnableMap, _navigatorPopulationGenomesDataLogger, _populationLoggingBatchInterval,
_isNavigatorSpecieFixedSize);
// Create the maze queueing evolution algorithm
AbstractEvolutionAlgorithm<MazeGenome> mazeEvolutionAlgorithm =
new QueueingNeatEvolutionAlgorithm<MazeGenome>(
new NeatEvolutionAlgorithmParameters
{
SpecieCount = MazeNumSpecies,
MaxSpecieSize =
MazeNumSpecies > 0 ? MazeDefaultPopulationSize/MazeNumSpecies : MazeDefaultPopulationSize
},
new ParallelKMeansClusteringStrategy<MazeGenome>(new ManhattanDistanceMetric(1.0, 0.0, 10.0),
ParallelOptions), null,
MazeBatchSize, RunPhase.Primary, false, false, _mazeEvolutionDataLogger, _mazeLogFieldEnableMap,
_mazePopulationGenomesDataLogger, _populationLoggingBatchInterval, _isMazeSpecieFixedSize);
// Create the maze phenome evaluator
IPhenomeEvaluator<MazeStructure, BehaviorInfo> mazeEvaluator = new MazeEnvironmentMCSEvaluator(
_maxTimesteps,
_minSuccessDistance, BehaviorCharacterizationFactory, _numAgentSuccessCriteria, _numAgentFailedCriteria);
// Create navigator phenome evaluator
IPhenomeEvaluator<IBlackBox, BehaviorInfo> navigatorEvaluator = new MazeNavigatorMCSEvaluator(
_maxTimesteps, _minSuccessDistance, BehaviorCharacterizationFactory, _numMazeSuccessCriteria);
// Create maze genome decoder
IGenomeDecoder<MazeGenome, MazeStructure> mazeGenomeDecoder = new MazeDecoder(_mazeHeight, _mazeWidth,
_mazeScaleMultiplier);
// Create navigator genome decoder
IGenomeDecoder<NeatGenome, IBlackBox> navigatorGenomeDecoder = new NeatGenomeDecoder(ActivationScheme);
// Create the maze genome evaluator
IGenomeEvaluator<MazeGenome> mazeFitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<MazeGenome, MazeStructure>(mazeGenomeDecoder, mazeEvaluator,
SelectionType.Queueing, SearchType.MinimalCriteriaSearch, ParallelOptions);
// Create navigator genome evaluator
IGenomeEvaluator<NeatGenome> navigatorFitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(navigatorGenomeDecoder, navigatorEvaluator,
SelectionType.Queueing, SearchType.MinimalCriteriaSearch, ParallelOptions);
// Create the coevolution container
ICoevolutionAlgorithmContainer<NeatGenome, MazeGenome> coevolutionAlgorithmContainer =
new CoevolutionAlgorithmContainer<NeatGenome, MazeGenome>(neatEvolutionAlgorithm, mazeEvolutionAlgorithm);
// Initialize the container and component algorithms
coevolutionAlgorithmContainer.Initialize(navigatorFitnessEvaluator, genomeFactory1, seedAgentPopulation,
AgentDefaultPopulationSize, mazeFitnessEvaluator, genomeFactory2, genomeList2, MazeDefaultPopulationSize,
MaxGenerations, MaxEvaluations);
return coevolutionAlgorithmContainer;
}
