/// <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)
        {
            FileDataLogger logger = null;

            // 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, ParallelOptions);

            // Create complexity regulation strategy.
            var complexityRegulationStrategy =
                ExperimentUtils.CreateComplexityRegulationStrategy(ComplexityRegulationStrategy, Complexitythreshold);

            // Initialize the logger
            if (_generationalLogFile != null)
            {
                logger =
                    new FileDataLogger(_generationalLogFile);
            }

            // Create the evolution algorithm.
            var ea = new SteadyStateNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
                speciationStrategy, complexityRegulationStrategy, _batchSize, _populationEvaluationFrequency,
                RunPhase.Primary, logger);

            // Create IBlackBox evaluator.
            var mazeNavigationEvaluator = new MazeNavigationMCNSEvaluator(MaxDistanceToTarget, MaxTimesteps,
                MazeVariant,
                MinSuccessDistance, _behaviorCharacterizationFactory);

            // Create genome decoder.
            var genomeDecoder = CreateGenomeDecoder();

            // Create a novelty archive.
            AbstractNoveltyArchive<NeatGenome> archive =
                new BehavioralNoveltyArchive<NeatGenome>(_archiveAdditionThreshold,
                    _archiveThresholdDecreaseMultiplier, _archiveThresholdIncreaseMultiplier,
                    _maxGenerationArchiveAddition, _maxGenerationsWithoutArchiveAddition);

            //            IGenomeEvaluator<NeatGenome> fitnessEvaluator =
            //                new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
            //                    _nearestNeighbors, archive);

            IGenomeEvaluator<NeatGenome> fitnessEvaluator =
                new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
                    SelectionType.SteadyState,
                    SearchType.MinimalCriteriaNoveltySearch,
                    _nearestNeighbors, archive);

            // Initialize the evolution algorithm.
            ea.Initialize(fitnessEvaluator, genomeFactory, genomeList, 0, MaxEvaluations, archive);

            // 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)
        {
            FileDataLogger logger = null;

            // 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, ParallelOptions);

            // Create complexity regulation strategy.
            var complexityRegulationStrategy =
                ExperimentUtils.CreateComplexityRegulationStrategy(ComplexityRegulationStrategy, Complexitythreshold);

            // Initialize the logger
            if (_generationalLogFile != null)
            {
                logger =
                    new FileDataLogger(_generationalLogFile);
            }

            // Create the evolution algorithm.
            var ea = new SteadyStateNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
                speciationStrategy, complexityRegulationStrategy, _batchSize, _populationEvaluationFrequency,
                RunPhase.Primary, logger);

            // Create IBlackBox evaluator.
            var mazeNavigationEvaluator = new MazeNavigationRandomEvaluator(MaxDistanceToTarget, MaxTimesteps,
                MazeVariant, MinSuccessDistance);

            // Create genome decoder.
            var genomeDecoder = CreateGenomeDecoder();

            IGenomeEvaluator<NeatGenome> fitnessEvaluator =
                new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
                    ParallelOptions);

            // Initialize the evolution algorithm.
            ea.Initialize(fitnessEvaluator, genomeFactory, genomeList, null, MaxEvaluations);

            // Finished. Return the evolution algorithm
            return ea;
        }
        /// <summary>
        ///     Configures and instantiates the initialization evolutionary algorithm.
        /// </summary>
        /// <param name="parallelOptions">Synchronous/Asynchronous execution settings.</param>
        /// <param name="genomeList">The initial population of genomes.</param>
        /// <param name="genomeFactory">The genome factory initialized by the main evolution thread.</param>
        /// <param name="genomeDecoder">The decoder to translate genomes into phenotypes.</param>
        /// <param name="startingEvaluations">
        ///     The number of evaluations that preceeded this from which this process will pick up
        ///     (this is used in the case where we're restarting a run because it failed to find a solution in the allotted time).
        /// </param>
        public void InitializeAlgorithm(ParallelOptions parallelOptions, List<NeatGenome> genomeList,
            IGenomeFactory<NeatGenome> genomeFactory, IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder, ulong startingEvaluations)
        {
            // Set the boiler plate algorithm parameters
            base.InitializeAlgorithm(parallelOptions, genomeList, genomeDecoder, startingEvaluations);

            // Create the initialization evolution algorithm.
            InitializationEa = new SteadyStateNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
                SpeciationStrategy, ComplexityRegulationStrategy, _batchSize, _populationEvaluationFrequency,
                RunPhase.Initialization, _evolutionDataLogger, _initializationLogFieldEnableMap);

            // Create IBlackBox evaluator.
            MazeNavigationMCSInitializationEvaluator mazeNavigationEvaluator =
                new MazeNavigationMCSInitializationEvaluator(MaxDistanceToTarget, MaxTimesteps,
                    _mazeVariant,
                    MinSuccessDistance, _behaviorCharacterizationFactory, startingEvaluations);

            // Create a novelty archive.
            AbstractNoveltyArchive<NeatGenome> archive =
                new BehavioralNoveltyArchive<NeatGenome>(_archiveAdditionThreshold,
                    _archiveThresholdDecreaseMultiplier, _archiveThresholdIncreaseMultiplier,
                    _maxGenerationArchiveAddition, _maxGenerationsWithoutArchiveAddition);

            //                IGenomeEvaluator<NeatGenome> fitnessEvaluator =
            //                    new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
            //                        SelectionType.SteadyState, SearchType.NoveltySearch,
            //                        _nearestNeighbors, archive, _evaluationDataLogger, _serializeGenomeToXml);

            IGenomeEvaluator<NeatGenome> fitnessEvaluator =
                new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
                    SelectionType.SteadyState, SearchType.NoveltySearch,
                    _nearestNeighbors, archive, _evaluationDataLogger, _serializeGenomeToXml);

            // Only pull the number of genomes from the list equivalent to the initialization algorithm population size
            // (this is to handle the case where the list was created in accordance with the primary algorithm
            // population size, which could have been larger)
            genomeList = genomeList.Take(PopulationSize).ToList();

            // Replace genome factory primary NEAT parameters with initialization parameters
            ((NeatGenomeFactory)genomeFactory).ResetNeatGenomeParameters(NeatGenomeParameters);

            // Initialize the evolution algorithm.
            InitializationEa.Initialize(fitnessEvaluator, genomeFactory, genomeList, PopulationSize, null,
                _maxEvaluations + startingEvaluations,
                archive);
        }