Ejemplo n.º 1
0
        private Evolution initEvolution(ProgressReport pReport)
        {
            var e = new Evolution();

            e.Iteration  = pReport.Iteration;
            e.MaxFitness = pReport.IterationStatistics.MaximumFitness;
            e.AvgFitness = pReport.IterationStatistics.AverageFitness;
            e.Status     = pReport.IterationStatus;

            //
            return(e);
        }
Ejemplo n.º 2
0
 //default constructor
 public Factory()
 {
     m_progresReport = new ProgressReport()
     {
         BestIteration       = -1,
         BestSolution        = null, Iteration = -1, IterationStatus = IterationStatus.Initialize,
         IterationStatistics = new IterationStatistics()
         {
             AverageFitness = -1, MaximumFitness = -1
         }
     };
 }
Ejemplo n.º 3
0
        /// <summary>
        /// Constructor
        /// </summary>
        /// <param name="param"></param>
        /// <param name="funSet"></param>
        /// <param name="terSet"></param>
        public Factory(Parameters param, Function[] funSet, int[] terSet, CancellationToken token)
        {
            //create population
            m_Population       = new Population();
            m_Population.Token = token;
            m_History          = new List <Evolution>();

            //prepare progress report for the first time.
            m_progresReport                     = new ProgressReport();
            m_progresReport.Iteration           = 0;
            m_progresReport.IterationStatus     = IterationStatus.Initialize;
            m_progresReport.IterationStatistics = new IterationStatistics();
            m_progresReport.Message             = "GPFactory successfully created algorithm.";
            m_progresReport.BestSolution        = new Chromosome();
            m_progresReport.IterationStatistics.IterationSeconds = 0;

            //initialize main GP components parameters, function and terminals
            initMainComponents(param, funSet, terSet);
        }
Ejemplo n.º 4
0
        /// <summary>
        /// Calculate best chromosome in the current population (generation)
        /// </summary>
        /// <param name="chromosomes"></param>
        /// <param name="pr"></param>
        private void calculatePopulation(List <IChromosome> chromosomes, ProgressReport pr)
        {
            // calculate basic stat of the population
            float fitnessMax = float.MinValue;
            float fitnessSum = 0;

            if (chromosomes != null && chromosomes.Count > 0)
            {
                ProgresReport.BestSolution = setBestSolution(chromosomes[0]);
            }
            else
            {
                return;
            }

            int counter = 0;

            for (int i = 0; i < chromosomes.Count; i++)
            {
                if (float.IsNaN(chromosomes[i].Fitness))
                {
                    continue;
                }
                counter++;
                float fitness = chromosomes[i].Fitness;
                // sum calculation
                fitnessSum += fitness;

                // cal the best chromosome
                if (fitness > fitnessMax)
                {
                    fitnessMax      = fitness;
                    pr.BestSolution = setBestSolution(chromosomes[i]);
                }
            }

            float fitnessAvg = (float)Math.Round(fitnessSum / counter, 4);

            pr.IterationStatistics.AverageFitness = fitnessAvg;
            pr.IterationStatistics.MaximumFitness = fitnessMax;
        }
Ejemplo n.º 5
0
        /// <summary>
        /// perform one iteration evolution for the GP
        /// </summary>
        /// <param name="pr"></param>
        private void performIteration(ProgressReport pr, CancellationToken ct)
        {
            //mating preparation offspring population
            m_Population.PrepareForMating();

            Stopwatch sw  = Stopwatch.StartNew();
            string    str = $"It={pr.Iteration}; ";

            //1. operaton
            m_Population.Crossover(m_Parameters, m_FunctionSet, m_TerminalSet);
            str += $"Crossover ={sw.Elapsed.TotalSeconds.ToString("F3")} sec| ";
            sw.Restart();

            //2. operation
            m_Population.Mutate(m_Parameters, m_FunctionSet, m_TerminalSet);
            str += $"Mutation ={sw.Elapsed.TotalSeconds.ToString("F3")} sec| ";
            sw.Restart();

            //3. operation
            m_Population.Reproduction(m_Parameters, m_FunctionSet, m_TerminalSet);
            str += $"Reproduction ={sw.Elapsed.TotalSeconds.ToString("F3")} sec| ";
            sw.Restart();

            //4. operation
            m_Population.EvaluatePopulation(m_Parameters, pr.Iteration);
            str += $"FitnessCalc ={sw.Elapsed.TotalSeconds.ToString("F3")} sec| ";
            sw.Restart();

            //
            m_Population.CreateNewGeneration(m_Parameters, m_FunctionSet, m_TerminalSet);
            str += $"NewGen ={sw.Elapsed.TotalSeconds.ToString("F3")} sec| ";
            sw.Restart();
            //
            calculatePopulation(m_Population.m_Chromosomes, pr);
            str       += $"Calculation ={sw.Elapsed.TotalSeconds.ToString("F3")} sec| ";
            pr.Message = str;
        }
Ejemplo n.º 6
0
        public void GPFactoryFromString(string strFactory)
        {
            if (string.IsNullOrEmpty(strFactory))
            {
                return;
            }
            try
            {
                Evolution[]            hist = null;
                JsonSerializerSettings sett = new JsonSerializerSettings();
                sett.NullValueHandling = NullValueHandling.Ignore;

                var obj = JsonConvert.DeserializeObject(strFactory, sett);

                var param = ((dynamic)obj)["m_Parameters"] as Newtonsoft.Json.Linq.JObject;
                var ter   = ((dynamic)obj)["m_TerminalSet"] as Newtonsoft.Json.Linq.JArray;
                var fun   = ((dynamic)obj)["m_FunctionSet"] as Newtonsoft.Json.Linq.JArray;
                var prog  = ((dynamic)obj)["m_progresReport"] as Newtonsoft.Json.Linq.JObject;
                var tc    = ((dynamic)obj)["m_TerminationCriteria"] as Newtonsoft.Json.Linq.JObject;
                var hi    = ((dynamic)obj)["history"] as Newtonsoft.Json.Linq.JArray;
                var pop   = ((dynamic)obj)["popStr"] as Newtonsoft.Json.Linq.JValue;

                if (param != null)
                {
                    m_Parameters = param.ToObject <Parameters>();
                }
                if (ter != null)
                {
                    m_TerminalSet = ter.ToObject <int[]>();
                }
                if (fun != null)
                {
                    m_FunctionSet = fun.ToObject <Function[]>();
                }
                if (prog != null)
                {
                    m_progresReport = prog.ToObject <ProgressReport>();
                }
                if (tc != null)
                {
                    m_TerminationCriteria = tc.ToObject <TerminationCriteria>();
                }
                if (hi != null)
                {
                    hist = hi.ToObject <Evolution[]>();
                }

                //create history
                if (hist != null)
                {
                    m_History = new List <Evolution>(hist);
                }

                m_Population = new Population();

                if (pop != null)
                {
                    var popStr = pop.ToObject <string>();
                    m_Population.PopulationFromString(popStr);
                }
            }
            catch (Exception)
            {
                throw;
            }
        }