static void RunTrial(int offset)
{
RESULTS_FILE = EXPERIMENTS_DIR + RESULTS_FILE_BASE + offset + ".csv";
_random = new FastRandom();
_experiment = new SocialExperiment();
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(CONFIG_FILE);
_experiment.Initialize("SimpleEvolution", xmlConfig.DocumentElement);
_experiment.NeatGenomeParameters.AddConnectionMutationProbability = 0;
_experiment.NeatGenomeParameters.AddNodeMutationProbability = 0;
_experiment.NeatGenomeParameters.DeleteConnectionMutationProbability = 0;
SocialExperiment.CreateNetwork(FEED_FORWARD_NETWORK_FILE, _experiment.InputCount, _experiment.OutputCount);
// Record the changes at each step
_experiment.World.Stepped += new social_learning.World.StepEventHandler(World_Stepped);
// Read in the seed genome from file. This is the prototype for our other population of networks.
var seed = _experiment.LoadPopulation(XmlReader.Create(FEED_FORWARD_NETWORK_FILE))[0];
// Create a genome factory with our neat genome parameters object and the appropriate number of input and output neuron genes.
IGenomeFactory <NeatGenome> genomeFactory = _experiment.CreateGenomeFactory();
// Create an initial population of randomly generated genomes.
List <NeatGenome> genomeList = genomeFactory.CreateGenomeList(_experiment.DefaultPopulationSize, 0, seed);
// Randomize the genomes
RandomizeGenomes(genomeList);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = _experiment.CreateGenomeDecoder();
// Create the evaluator that will handle the simulation
_evaluator = new ForagingEvaluator <NeatGenome>(genomeDecoder, _experiment.World, AgentTypes.Social)
{
MaxTimeSteps = 200000UL,
BackpropEpochsPerExample = 1
};
using (TextWriter writer = new StreamWriter(RESULTS_FILE))
writer.WriteLine("Step,Best,Average");
// Start the simulation
_evaluator.Evaluate(genomeList);
}
static void RunTrial(int offset)
{
RESULTS_FILE = EXPERIMENTS_DIR + RESULTS_FILE_BASE + offset + ".csv";
_random = new FastRandom();
_experiment = new SocialExperiment();
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(CONFIG_FILE);
_experiment.Initialize("SimpleEvolution", xmlConfig.DocumentElement);
_experiment.NeatGenomeParameters.AddConnectionMutationProbability = 0;
_experiment.NeatGenomeParameters.AddNodeMutationProbability = 0;
_experiment.NeatGenomeParameters.DeleteConnectionMutationProbability = 0;
SocialExperiment.CreateNetwork(FEED_FORWARD_NETWORK_FILE, _experiment.InputCount, _experiment.OutputCount);
// Record the changes at each step
_experiment.World.Stepped += new social_learning.World.StepEventHandler(World_Stepped);
// Read in the seed genome from file. This is the prototype for our other population of networks.
var seed = _experiment.LoadPopulation(XmlReader.Create(FEED_FORWARD_NETWORK_FILE))[0];
// Create a genome factory with our neat genome parameters object and the appropriate number of input and output neuron genes.
IGenomeFactory<NeatGenome> genomeFactory = _experiment.CreateGenomeFactory();
// Create an initial population of randomly generated genomes.
List<NeatGenome> genomeList = genomeFactory.CreateGenomeList(_experiment.DefaultPopulationSize, 0, seed);
// Randomize the genomes
RandomizeGenomes(genomeList);
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = _experiment.CreateGenomeDecoder();
// Create the evaluator that will handle the simulation
_evaluator = new ForagingEvaluator<NeatGenome>(genomeDecoder, _experiment.World, AgentTypes.Social)
{
MaxTimeSteps = 200000UL,
BackpropEpochsPerExample = 1
};
using (TextWriter writer = new StreamWriter(RESULTS_FILE))
writer.WriteLine("Step,Best,Average");
// Start the simulation
_evaluator.Evaluate(genomeList);
}
static void Main(string[] args)
{
_random = new FastRandom();
_experiment = new SocialExperiment();
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(CONFIG_FILE);
_experiment.Initialize("SimpleEvolution", xmlConfig.DocumentElement);
_experiment.NeatGenomeParameters.AddConnectionMutationProbability = 0;
_experiment.NeatGenomeParameters.AddNodeMutationProbability = 0;
_experiment.NeatGenomeParameters.DeleteConnectionMutationProbability = 0;
SocialExperiment.CreateNetwork(FEED_FORWARD_NETWORK_FILE, _experiment.InputCount, 20, _experiment.OutputCount);
// Record the changes at each step
_experiment.World.Stepped += new social_learning.World.StepEventHandler(World_Stepped);
// Read in the teacher genome from file.
var agentGenome = _experiment.LoadPopulation(XmlReader.Create(FEED_FORWARD_NETWORK_FILE));
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = _experiment.CreateGenomeDecoder();
// Create the evaluator that will handle the simulation
_evaluator = new ForagingEvaluator <NeatGenome>(genomeDecoder, _experiment.World, AgentTypes.QLearning)
{
MaxTimeSteps = 50000000UL,
BackpropEpochsPerExample = 1
};
using (TextWriter writer = new StreamWriter(RESULTS_FILE))
writer.WriteLine("Step,Score");
// Start the simulation
_evaluator.Evaluate(agentGenome);
}
static void Main(string[] args)
{
_random = new FastRandom();
_experiment = new SocialExperiment();
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(CONFIG_FILE);
_experiment.Initialize("SimpleEvolution", xmlConfig.DocumentElement);
_experiment.NeatGenomeParameters.AddConnectionMutationProbability = 0;
_experiment.NeatGenomeParameters.AddNodeMutationProbability = 0;
_experiment.NeatGenomeParameters.DeleteConnectionMutationProbability = 0;
SocialExperiment.CreateNetwork(FEED_FORWARD_NETWORK_FILE, _experiment.InputCount, 20, _experiment.OutputCount);
// Record the changes at each step
_experiment.World.Stepped += new social_learning.World.StepEventHandler(World_Stepped);
// Read in the teacher genome from file.
var agentGenome = _experiment.LoadPopulation(XmlReader.Create(FEED_FORWARD_NETWORK_FILE));
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = _experiment.CreateGenomeDecoder();
// Create the evaluator that will handle the simulation
_evaluator = new ForagingEvaluator<NeatGenome>(genomeDecoder, _experiment.World, AgentTypes.QLearning)
{
MaxTimeSteps = 50000000UL,
BackpropEpochsPerExample = 1
};
using (TextWriter writer = new StreamWriter(RESULTS_FILE))
writer.WriteLine("Step,Score");
// Start the simulation
_evaluator.Evaluate(agentGenome);
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome2 population and their associated/parent genome2 factory.
/// </summary>
public NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy<NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy<NeatGenome>(distanceMetric, new ParallelOptions());
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = new NullComplexityRegulationStrategy();// ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm<NeatGenome> ea = new NeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the phenome evaluator.
_evaluator = new ForagingEvaluator<NeatGenome>(genomeDecoder, _world, _agentType, _navigationEnabled, _hidingEnabled)
{
MaxTimeSteps = _timeStepsPerGeneration,
EvoParadigm = _paradigm,
MemParadigm = _memory,
GenerationsPerMemorySize = _memGens,
MaxMemorySize = _maxMemorySize,
TeachParadigm = _teaching,
TrialId = TrialId,
PredatorCount = _predCount,
PredatorDistribution = PredatorDistribution,
PredatorTypes = _predTypes,
PredatorGenerations = _predGens,
DistinguishPredators = _distinguishPreds,
LogDiversity = _logDiversity
};
// Initialize the evolution algorithm.
ea.Initialize(_evaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
