/// <summary>
/// Runs the initialization algorithm until the specified number of viable genomes (i.e. genomes that meets the minimal
/// criteria) are found and returns those genomes along with the total number of evaluations that were executed to find
/// them.
/// </summary>
/// <param name="totalEvaluations">The resulting number of evaluations to find the viable seed genomes.</param>
/// <param name="maxEvaluations">
/// The maximum number of evaluations that can be executed before the initialization process
/// is restarted. This prevents getting stuck for a long time and/or ending up with unecessarily complex networks.
/// </param>
/// <param name="restartCount">
/// The number of times the initialization process has been restarted (this is only used for
/// status logging purposes).
/// </param>
/// <returns>The list of seed genomes that meet the minimal criteria.</returns>
public override List <NeatGenome> RunEvolution(out ulong totalEvaluations, uint?maxEvaluations,
uint restartCount)
{
// Create list of viable genomes
List <NeatGenome> viableGenomes = new List <NeatGenome>(MinSuccessfulAgentCount + MinUnsuccessfulAgentCount);
do
{
Console.Out.WriteLine("Starting up the algorithm on restart #{0}", restartCount);
// Start the algorithm
InitializationEa.StartContinue();
Console.Out.WriteLine("Going into algorithm wait loop...");
// Ping for status every few hundred milliseconds
while (RunState.Terminated != InitializationEa.RunState &&
RunState.Paused != InitializationEa.RunState)
{
if (InitializationEa.CurrentEvaluations >= maxEvaluations)
{
// Record the total number of evaluations
totalEvaluations = InitializationEa.CurrentEvaluations;
// Halt the EA worker thread
InitializationEa.RequestPauseAndWait();
// Null out the EA and delete the thread
// (it's necessary to null out these resources so that the thread will be completely garbage collected)
InitializationEa.Reset();
InitializationEa = null;
// Note that the calling experiment must be able to handle this null return value (not great practice)
return(null);
}
Thread.Sleep(200);
}
Console.Out.WriteLine("Attempting to extract viable genome from list...");
// Add all of the genomes that have solved the maze
viableGenomes.AddRange(
InitializationEa.GenomeList.Where(
genome =>
genome.EvaluationInfo != null &&
genome.EvaluationInfo.ObjectiveDistance < MinSuccessDistance)
.Take(MinSuccessfulAgentCount));
Console.Out.WriteLine("Extracted [{0}] of [{1}] viable genomes in [{2}] evaluations",
viableGenomes.Count, MinSuccessfulAgentCount, InitializationEa.CurrentEvaluations);
} while (viableGenomes.Count < MinSuccessfulAgentCount);
// Add the remainder of genomes who have not solved the maze
// (note that the intuition for doing this after the loop is that most will not have solved)
viableGenomes.AddRange(
InitializationEa.GenomeList.Where(
genome =>
genome.EvaluationInfo != null &&
genome.EvaluationInfo.ObjectiveDistance > MinSuccessDistance)
.Take(MinUnsuccessfulAgentCount));
// Ensure that the above statement was able to get the required number of unsuccessful agent genomes
if (viableGenomes.Count(genome => genome.EvaluationInfo.ObjectiveDistance > MinSuccessDistance) <
MinUnsuccessfulAgentCount)
{
throw new SharpNeatException(
"Novelty search MCC initialization algorithm failed to produce the requisite number of unsuccessful agent genomes.");
}
// Set the total number of evaluations that were executed as part of the initialization process
totalEvaluations = InitializationEa.CurrentEvaluations;
return(viableGenomes);
}
/// <summary>
/// Runs the initialization algorithm until the specified number of viable genomes (i.e. genomes that meets the minimal
/// criteria) are found and returns those genomes along with the total number of evaluations that were executed to find
/// them.
/// </summary>
/// <param name="totalEvaluations">The resulting number of evaluations to find the viable seed genomes.</param>
/// <param name="maxEvaluations">
/// The maximum number of evaluations that can be executed before the initialization process
/// is restarted. This prevents getting stuck for a long time and/or ending up with unecessarily complex networks.
/// </param>
/// <param name="restartCount">
/// The number of times the initialization process has been restarted (this is only used for
/// status logging purposes).
/// </param>
/// <returns>The list of seed genomes that meet the minimal criteria.</returns>
public override List <NeatGenome> RunEvolution(out ulong totalEvaluations, uint?maxEvaluations,
uint restartCount)
{
// The minimum fitness for an agent who has solved the maze
int solvedFitness = MaxDistanceToTarget - MinSuccessDistance;
// Create list of viable genomes
List <NeatGenome> viableGenomes = new List <NeatGenome>(MinSuccessfulAgentCount + MinUnsuccessfulAgentCount);
Console.Out.WriteLine("Starting up the algorithm on restart #{0}", restartCount);
do
{
// Start the algorithm
InitializationEa.StartContinue();
// Ping for status every few hundred milliseconds
while (RunState.Terminated != InitializationEa.RunState &&
RunState.Paused != InitializationEa.RunState)
{
Console.WriteLine(@"Current Evaluations: {0} Mean Complexity: {1} Closest Genome Distance: {2}",
InitializationEa.CurrentEvaluations, InitializationEa.Statistics.MeanComplexity,
MaxDistanceToTarget - InitializationEa.Statistics.MaxFitness);
if (InitializationEa.CurrentEvaluations >= maxEvaluations)
{
// Record the total number of evaluations
totalEvaluations = InitializationEa.CurrentEvaluations;
// Halt the EA worker thread
InitializationEa.RequestPauseAndWait();
// Null out the factory/EA and delete the thread
// (it's necessary to null out these resources so that the thread will be completely garbage collected)
InitializationEa.Reset();
InitializationEa = null;
// Note that the calling experiment must be able to handle this null return value (not great practice)
return(null);
}
Thread.Sleep(200);
}
// Add all of the genomes that have solved the maze
viableGenomes.AddRange(
InitializationEa.GenomeList.Where(genome => genome.EvaluationInfo.Fitness > solvedFitness)
.Take(MinSuccessfulAgentCount));
} while (viableGenomes.Count < MinSuccessfulAgentCount);
// Add the remainder of genomes who have not solved the maze
// (note that the intuition for doing this after the loop is that most will not have solved)
viableGenomes.AddRange(
InitializationEa.GenomeList.Where(genome => genome.EvaluationInfo.Fitness < solvedFitness)
.Take(MinUnsuccessfulAgentCount));
// Ensure that the above statement was able to get the required number of unsuccessful agent genomes
if (viableGenomes.Count(genome => genome.EvaluationInfo.Fitness < solvedFitness) <
MinUnsuccessfulAgentCount)
{
throw new SharpNeatException(
"Fitness MCC initialization algorithm failed to produce the requisite number of unsuccessful agent genomes.");
}
// Set the total number of evaluations that were executed as part of the initialization process
totalEvaluations = InitializationEa.CurrentEvaluations;
return(viableGenomes);
}
