public override void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
        {
            // Evaluate in single-file each genome within the population.
            // Only evaluate new genomes (those with EvaluationCount==0).
            int count = pop.GenomeList.Count;

            for (int i = 0; i < count; i++)
            {
                // Grab the next individual out of the population
                IGenome g = pop.GenomeList[i];
                if (g.EvaluationCount != 0)
                {
                    continue;
                }

                // The current genome is a CPPN genome, not a network genome
                // So, decode the CPPN genome into a CPPN, use the CPPN to generate an ANN,
                // then run the networkEvaluator on the ANN
                INetwork cppn = g.Decode(activationFn);
                if (cppn == null)
                {       // Future genomes may not decode - handle the possibility.
                    g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
                }
                else
                {
                    //BehaviorType behavior;
                    //INetwork network = Chromaria.Simulator.controllerSubstrate.generateGenome(cppn).Decode(activationFn);
                    g.Fitness = Math.Max(0.0f, EvolutionAlgorithm.MIN_GENOME_FITNESS);
                    //if (Chromaria.Simulator.plantingWasValid)
                    //Chromaria.Simulator.successfulPlanterGenome = g;
                    g.RealFitness = g.Fitness;
                }

                // Reset these genome level statistics.
                g.TotalFitness    = g.Fitness;
                g.EvaluationCount = 1;

                // Update master evaluation counter.
                evaluationCount++;

                // Close the XML tag for this individual
                //Chromaria.Simulator.xmlWriter.WriteEndElement();
            }
            if (ea.NeatParameters.noveltySearch)
            {
                if (ea.NeatParameters.noveltySearch && ea.noveltyInitialized)
                {
                    ea.CalculateNovelty();
                }
            }
        }
        public virtual void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
        {
            // Evaluate in single-file each genome within the population.
            // Only evaluate new genomes (those with EvaluationCount==0).
            int count = pop.GenomeList.Count;

            for (int i = 0; i < count; i++)
            {
                IGenome g = pop.GenomeList[i];
                if (g.EvaluationCount != 0)
                {
                    continue;
                }

                INetwork network = g.Decode(activationFn);
                if (network == null)
                {                       // Future genomes may not decode - handle the possibility.
                    g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
                }
                else
                {
                    BehaviorType behavior;
                    g.Fitness     = Math.Max(networkEvaluator.EvaluateNetwork(network, out behavior), EvolutionAlgorithm.MIN_GENOME_FITNESS);
                    g.RealFitness = g.Fitness;
                    g.Behavior    = behavior;
                }

                // Reset these genome level statistics.
                g.TotalFitness    = g.Fitness;
                g.EvaluationCount = 1;

                // Update master evaluation counter.
                evaluationCount++;
            }

            if (ea.NeatParameters.noveltySearch)
            {
                if (ea.NeatParameters.noveltySearch && ea.noveltyInitialized)
                {
                    ea.CalculateNovelty();
                }
            }
        }
Exemple #3
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        public void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
        {
            int      count = pop.GenomeList.Count;
            evalPack e;
            IGenome  g;
            int      i;

            for (i = 0; i < count; i++)
            {
                //Console.WriteLine(i);
                sem.WaitOne();
                g = pop.GenomeList[i];
                e = new evalPack(networkEvaluator, activationFn, g, i % HyperNEATParameters.numThreads, (int)ea.Generation);
                ThreadPool.QueueUserWorkItem(new WaitCallback(evalNet), e);
                // Update master evaluation counter.
                evaluationCount++;

                /*if(printFinalPositions)
                 *  file.WriteLine(g.Behavior.behaviorList[0].ToString() + ", " + g.Behavior.behaviorList[1].ToString());//*/
            }
            //Console.WriteLine("waiting for last threads..");
            for (int j = 0; j < HyperNEATParameters.numThreads; j++)
            {
                sem.WaitOne();
                //  Console.WriteLine("waiting");
            }
            for (int j = 0; j < HyperNEATParameters.numThreads; j++)
            {
                //Console.WriteLine("releasing");

                sem.Release();
            }
            //Console.WriteLine("generation done...");
            //calulate novelty scores...
            if (ea.NeatParameters.noveltySearch)
            {
                if (ea.NeatParameters.noveltySearch)
                {
                    ea.CalculateNovelty();
                }
            }
        }