private void evolve_Click(object sender, EventArgs e)
{
if (btnEvolve.Text == "Stop!")
{
stopEvolution();
return;
}
running = true;
_experiment = new SocialExperiment();
// Load config XML.
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(_configFile);
_experiment.Initialize("SimpleEvolution", xmlConfig.DocumentElement);
_experiment.PlantLayout = _plantLayout;
_experiment.World.Changed += new World.ChangedEventHandler(world_Changed);
_experiment.World.AgentEaten += new World.AgentEatenHandler(World_AgentEaten);
btnEvolve.Text = "Stop!";
// Start the evolution
if (_configFile == QLEARNING_CONFIG_FILE)
{
qLearningThread = new Thread(new ThreadStart(startQLearning));
qLearningThread.Start();
}
else
{
qLearningThread = new Thread(new ThreadStart(startEvolution));
qLearningThread.Start();
}
}
void RunExperiment(string XMLFile, string filename)
{
_filename = filename;
_experiment = new SocialExperiment();
// Write the header for the results file in CSV format.
using (TextWriter writer = new StreamWriter(_filename))
writer.WriteLine("Generation,Average,Best,Updates");
using (TextWriter writer = new StreamWriter(_filename.Replace(".csv", "_diversity_after.csv")))
writer.WriteLine("Generation,Orientation Variance,Velocity Variance");
using (TextWriter writer = new StreamWriter(_filename.Replace(".csv", "_diversity_before.csv")))
writer.WriteLine("Generation,Orientation Variance,Velocity Variance");
// Load the XML configuration file
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(XMLFile);
_experiment.Initialize("SimpleEvolution", xmlConfig.DocumentElement);
// Create the evolution algorithm and attach the update event.
_ea = _experiment.CreateEvolutionAlgorithm();
_ea.UpdateScheme = new SharpNeat.Core.UpdateScheme(1);
_ea.UpdateEvent += new EventHandler(_ea_UpdateEvent);
_experiment.Evaluator.TrialId = _trialNum;
_experiment.Evaluator.DiversityFile = _filename.Replace(".csv", "_diversity.csv");
// Start algorithm (it will run on a background thread).
_ea.StartContinue();
}
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);
}