Ejemplo n.º 1
0
        /// <summary>
        ///     Setup and solve the TSP.
        /// </summary>
        public void Solve()
        {
            IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
            var builder = new StringBuilder();

            InitCities(rnd);

            IPopulation pop = InitPopulation(rnd);

            IScoreFunction score = new TSPScore(_cities);

            _genetic = new BasicEA(pop, score);

            _genetic.AddOperation(0.9, new SpliceNoRepeat(Cities / 3));
            _genetic.AddOperation(0.1, new MutateShuffle());

            int sameSolutionCount = 0;
            int iteration = 1;
            double lastSolution = double.MaxValue;

            while (sameSolutionCount < MaxSameSolution)
            {
                _genetic.Iteration();

                double thisSolution = _genetic.LastError;

                builder.Length = 0;
                builder.Append("Iteration: ");
                builder.Append(iteration++);
                builder.Append(", Best Path Length = ");
                builder.Append(thisSolution);

                Console.WriteLine(builder.ToString());

                if (Math.Abs(lastSolution - thisSolution) < 1.0)
                {
                    sameSolutionCount++;
                }
                else
                {
                    sameSolutionCount = 0;
                }

                lastSolution = thisSolution;
            }

            Console.WriteLine("Good solution found:");
            var best = (IntegerArrayGenome)_genetic.BestGenome;
            DisplaySolution(best);
            _genetic.FinishTraining();
        }
Ejemplo n.º 2
0
        /// <summary>
        ///     Process the specified file.
        /// </summary>
        /// <param name="filename">The filename to process.</param>
        public void Process(String filename)
        {
            // read the data from the resources
            Assembly assembly = Assembly.GetExecutingAssembly();
            Stream res = assembly.GetManifestResourceStream("AIFH_Vol2.Resources.simple-poly.csv");

            // did we fail to read the resouce
            if (res == null)
            {
                Console.WriteLine("Can't read iris data from embedded resources.");
                return;
            }

            // load the data
            var istream = new StreamReader(res);
            DataSet ds = DataSet.Load(istream);
            istream.Close();


            // Extract supervised training.
            IList<BasicData> training = ds.ExtractSupervised(0, 1, 1, 1);


            IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
            var eval = new EvaluateExpression(rnd);
            IPopulation pop = InitPopulation(rnd, eval);
            IScoreFunction score = new ScoreSmallExpression(training, 30);

            IEvolutionaryAlgorithm genetic = new BasicEA(pop, score);
            genetic.AddOperation(0.3, new MutateTree(3));
            genetic.AddOperation(0.7, new CrossoverTree());
            genetic.ShouldIgnoreExceptions = false;


            int sameSolutionCount = 0;
            int iteration = 1;
            double lastSolution = double.MaxValue;
            var builder = new StringBuilder();

            while (sameSolutionCount < MaxSameSolution && iteration < 1000)
            {
                genetic.Iteration();

                double thisSolution = genetic.LastError;

                builder.Length = 0;
                builder.Append("Iteration: ");
                builder.Append(iteration++);
                builder.Append(", Current error = ");
                builder.Append(thisSolution);
                builder.Append(", Best Solution Length = ");
                builder.Append(genetic.BestGenome.Count);

                Console.WriteLine(builder.ToString());

                if (Math.Abs(lastSolution - thisSolution) < 1.0)
                {
                    sameSolutionCount++;
                }
                else
                {
                    sameSolutionCount = 0;
                }

                lastSolution = thisSolution;
            }

            Console.WriteLine("Good solution found:");
            var best = (TreeGenome) genetic.BestGenome;
            Console.WriteLine(eval.DisplayExpressionNormal(best.Root));
            genetic.FinishTraining();
        }