C# IGenomeFactory类代码示例

        /// <summary>
        ///     Create and return a GenerationalNeatEvolutionAlgorithm 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>
        /// <returns>Constructed evolutionary algorithm</returns>
        public override INeatEvolutionAlgorithm<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, ParallelOptions);

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

            // Create the evolution algorithm.
            var ea = new GenerationalNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters, speciationStrategy,
                complexityRegulationStrategy);

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

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

            // Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
            IGenomeEvaluator<NeatGenome> fitnessEvaluator =
                new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
                    ParallelOptions);

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

            // 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 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)
        {
            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 GenerationalNeatEvolutionAlgorithm 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 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);

            // Create the evolution algorithm.
            var ea = new GenerationalNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
                speciationStrategy,
                complexityRegulationStrategy);

            // Create IBlackBox evaluator.
            var mazeNavigationEvaluator = new MazeNavigationNoveltyEvaluator(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);

            // Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
            //            IGenomeFitnessEvaluator<NeatGenome> fitnessEvaluator =
            //                new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator, _nearestNeighbors, archive);
            IGenomeEvaluator<NeatGenome> fitnessEvaluator =
                new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
                    SelectionType.Generational, SearchType.NoveltySearch,
                    ParallelOptions, _nearestNeighbors, archive);

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

            // Finished. Return the evolution algorithm
            return ea;
        }

 public override NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
 {
     var ea = DefaultNeatEvolutionAlgorithm;
     var evaluator = new LocalXorEvaluator();
     // Create genome decoder.
     IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
     // Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
     IGenomeListEvaluator<NeatGenome> innerEvaluator = new SerialGenomeListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator);
     // Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
     // that were in the population in previous generations (elite genomes). This is determined by examining each genome's evaluation info object.
     IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(
                                                                             innerEvaluator,
                                                                             SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
     // Initialize the evolution algorithm.
     ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
     // 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;
        }

 public override NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
 {
     // Create the evolution algorithm.
     var ea = DefaultNeatEvolutionAlgorithm;
     var evaluator = new RemoteBatchSixMultiplexerEvaluator(ea);
     IGenomeDecoder<NeatGenome, FastCyclicNetwork> genomeDecoder = _decoder;
     // Evaluates list of phenotypes
     IGenomeListEvaluator<NeatGenome> innerEvaluator =
         new BatchGenomeListEvaluator<NeatGenome, FastCyclicNetwork>(
             genomeDecoder,
             evaluator);
     // Weeds down the list to be evaluated
     IGenomeListEvaluator<NeatGenome> selectiveEvaluator = 
         new SelectiveGenomeListEvaluator<NeatGenome>(
             innerEvaluator,
             SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
     ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
     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;
        }

        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 KMeansClusteringStrategy<NeatGenome>(distanceMetric);

            // Create complexity regulation strategy.
            IComplexityRegulationStrategy complexityRegulationStrategy =
                ExperimentUtils.CreateComplexityRegulationStrategy("absolute", 10);

            // Create the evolution algorithm.
            var ea = new NeatEvolutionAlgorithm<NeatGenome>(
                    NeatEvolutionAlgorithmParameters,
                    speciationStrategy,
                    complexityRegulationStrategy);

            // Create IBlackBox evaluator.
            var evaluator = new RemoteXorEvaluator();

            // Create genome decoder.
            IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();

            // Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
            IGenomeListEvaluator<NeatGenome> innerEvaluator = new SerialGenomeListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator);

            // Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
            // that were in the population in previous generations (elite genomes). This is determined by examining each genome's evaluation info object.
            IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(
                                                                                    innerEvaluator,
                                                                                    SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
            // Initialize the evolution algorithm.
            ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);

            // Finished. Return the evolution algorithm
            return ea;
        }

    public NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
    {
        IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
        ISpeciationStrategy<NeatGenome> speciationStrategy = new KMeansClusteringStrategy<NeatGenome>(distanceMetric);

        IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);

        NeatEvolutionAlgorithm<NeatGenome> ea = new NeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);

        // Create black box evaluator
        SimpleEvaluator evaluator = new SimpleEvaluator(_optimizer);

        IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();

        IGenomeListEvaluator<NeatGenome> innerEvaluator = new UnityParallelListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _optimizer);

        IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(innerEvaluator,
            SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());

        //ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
        ea.Initialize(innerEvaluator, genomeFactory, genomeList);

        return ea;
    }

        /// <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 genome population and their associated/parent genome 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, _parallelOptions);

            // Create complexity regulation strategy.
            IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);

            // Create the evolution algorithm.
            NeatEvolutionAlgorithm<NeatGenome> ea = new NeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);

            // Create IBlackBox evaluator.
            DoublePoleBalancingEvaluator evaluator;
            switch(_variantStr)
            {
                case "DoublePole":
                    evaluator = new DoublePoleBalancingEvaluator();
                    break;
                case "DoublePoleNv":
                    evaluator = new DoublePoleBalancingEvaluatorNv();
                    break;
                case "DoublePoleNvAntiWiggle":
                    evaluator = new DoublePoleBalancingEvaluatorNvAntiWiggle();
                    break;
                default:
                    throw new SharpNeatException(string.Format("DoublePoleBalancing experiment config XML specifies unknown variant [{0}]", _variantStr));
            }

            // Create genome decoder.
            IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();

            // Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
            IGenomeListEvaluator<NeatGenome> innerEvaluator = new ParallelGenomeListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);

            // Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
            // that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
            IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(
                                                                                    innerEvaluator,
                                                                                    SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
            // Initialize the evolution algorithm.
            ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);

            // Finished. Return the evolution algorithm
            return ea;
        }

        /// <summary>
        ///     Evolves the requisite number of agents who satisfy the MC of the given maze.
        /// </summary>
        /// <param name="genomeFactory">The agent genome factory.</param>
        /// <param name="seedAgentList">The seed population of agents.</param>
        /// <param name="mazeStructure">The maze structure on which agents are to be evaluated.</param>
        /// <returns></returns>
        private List<NeatGenome> EvolveViableAgents(IGenomeFactory<NeatGenome> genomeFactory,
            List<NeatGenome> seedAgentList, MazeStructure mazeStructure)
        {
            List<NeatGenome> viableMazeAgents;
            uint restartCount = 0;
            ulong initializationEvaluations;

            do
            {
                // Instantiate the internal initialization algorithm
                _mazeNavigationInitializer.InitializeAlgorithm(ParallelOptions, seedAgentList.ToList(), genomeFactory,
                    mazeStructure, new NeatGenomeDecoder(ActivationScheme), 0);

                // Run the initialization algorithm until the requested number of viable seed genomes are found
                viableMazeAgents = _mazeNavigationInitializer.EvolveViableGenomes(out initializationEvaluations,
                    _maxInitializationEvaluations, restartCount);

                restartCount++;

                // Repeat if maximum allotted evaluations is exceeded
            } while (_maxInitializationEvaluations != null && viableMazeAgents == null &&
                     initializationEvaluations > _maxInitializationEvaluations);

            return viableMazeAgents;
        }

        /// <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 agent specie 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 < _numAgentSuccessCriteria*AgentNumSpecies)
            {
                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, (_numAgentSuccessCriteria*AgentNumSpecies));

                // 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);
            }

            // Reset primary NEAT genome parameters on agent genome factory
            ((NeatGenomeFactory) genomeFactory1).ResetNeatGenomeParameters(NeatGenomeParameters);

            // Create the NEAT (i.e. navigator) queueing evolution algorithm
            AbstractEvolutionAlgorithm<NeatGenome> neatEvolutionAlgorithm =
                new MultiQueueNeatEvolutionAlgorithm<NeatGenome>(
                    new NeatEvolutionAlgorithmParameters
                    {
                        SpecieCount = AgentNumSpecies,
                        MaxSpecieSize = AgentDefaultPopulationSize/AgentNumSpecies
                    },
                    new ParallelKMeansClusteringStrategy<NeatGenome>(new ManhattanDistanceMetric(1.0, 0.0, 10.0),
                        ParallelOptions), null, NavigatorBatchSize, RunPhase.Primary, _navigatorEvolutionDataLogger,
                    _navigatorLogFieldEnableMap, _navigatorPopulationGenomesDataLogger, _populationLoggingBatchInterval);
//.........这里部分代码省略.........

        private void btnCreateRandomPop_Click(object sender, EventArgs e)
        {
            // Parse population size and interconnection proportion from GUI fields.
            int? popSize = ParseInt(txtParamPopulationSize);
            if(null == popSize) {
                return;
            }

            double? initConnProportion = ParseDouble(txtParamInitialConnectionProportion);
            if(null == initConnProportion) {
                return;
            }

            INeatExperiment experiment = GetSelectedExperiment();
            experiment.NeatGenomeParameters.InitialInterconnectionsProportion = initConnProportion.Value;

            // Create a genome factory appropriate for the experiment.
            IGenomeFactory<NeatGenome> genomeFactory = experiment.CreateGenomeFactory();
                
            // Create an initial population of randomly generated genomes.
            List<NeatGenome> genomeList = genomeFactory.CreateGenomeList(popSize.Value, 0u);

            // Assign population to form variables & update GUI appropriately.
            _genomeFactory = genomeFactory;
            _genomeList = genomeList;
            UpdateGuiState();
        }

        public NeatTrainer(ExperimentSettings experimentSettings, NeatEvolutionAlgorithmParameters evolutionAlgorithmParameters, TrainingGameSettings gameSettings)
        {
            var neuromonPhenomeEvaluator = new NeuromonEvaluator(gameSettings, experimentSettings);
            var neatGenomeParameters = new NeatGenomeParameters();

            _neuromonExperiment = new NeuromonExperiment(experimentSettings, evolutionAlgorithmParameters, neuromonPhenomeEvaluator, neatGenomeParameters);

            _genomeIo = new GenomeIo(_neuromonExperiment);
            _genomeFactory = _neuromonExperiment.CreateGenomeFactory();

            if (experimentSettings.LoadExistingPopulation)
            {
                _genomePopulation = _genomeIo.Read(experimentSettings.ExistingPopulationFilePath);
            }
            else
            {
                // Randomly generate a new population
                _genomePopulation = _genomeFactory.CreateGenomeList(experimentSettings.PopulationSize, 0);
            }

            _genomeIo.CacheChampion(_genomePopulation.OrderByDescending(g => g.EvaluationInfo.Fitness).First());

            _fitnessStagnationDetector = new FitnessStagnationDetector(experimentSettings.StagnationDetectionTriggerValue);

            _desiredFitness = experimentSettings.DesiredFitness;

            _previousGeneration = 0;
            _overallBestFitness = 0.0;
        }