using System;

using GeneticSharp;

​

namespace FunctionOptimizationWithGeneticSharp

{

    class MainClass

    {

        /// <summer>

        /// https://diegogiacomelli.com.br/function-optimization-with-geneticsharp/

        /// </summer>

        public static void Main(string[] args)

        {

            GeneticAlgorithm ga = DiagonalExample();

​

            ga.Start();

            ga.Stop();

​

            ga = TcoExample();

            ga.Start();

            ga.Stop();

​

            Console.ReadKey();           

        }

​

        private static GeneticAlgorithm TcoExample()

        {

            float maxWidth = 998f;

            float maxHeight = 680f;

​

            var chromosome = new FloatingPointChromosome(

                new double[] { 0, 0, 0, 0 },

                new double[] { maxWidth, maxHeight, maxWidth, maxHeight },

                new int[] { 10, 10, 10, 10 },

                new int[] { 0, 0, 0, 0 });

​

            var population = new Population(50, 100, chromosome);

​

            var fitness = new FuncFitness((c) =>

            {

                var fc = c as FloatingPointChromosome;

​

                var values = fc.ToFloatingPoints();

                var x1 = values[0];

                var y1 = values[1];

                var x2 = values[2];

                var y2 = values[3];

​

                return Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2));

            });

​

            var selection = new EliteSelection();

            var crossover = new UniformCrossover(0.5f);

            var mutation = new FlipBitMutation();

            var termination = new FitnessStagnationTermination(100);

​

            var ga = new GeneticAlgorithm(

                population,

                fitness,

                selection,

                crossover,

                mutation);

​

            ga.Termination = termination;

​

            Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");

​

            var latestFitness = 0.0;

​

            ga.GenerationRan += (sender, e) =>

            {

                var bestChromosome = ga.BestChromosome as FloatingPointChromosome;

                var bestFitness = bestChromosome.Fitness.Value;

​

                if (bestFitness != latestFitness)

                {

                    latestFitness = bestFitness;

                    var phenotype = bestChromosome.ToFloatingPoints();

​

                    Console.WriteLine(

                        "Generation {0,2}: ({1},{2}),({3},{4}) = {5}",

                        ga.GenerationsNumber,

                        phenotype[0],

                        phenotype[1],

                        phenotype[2],

                        phenotype[3],

                        bestFitness

                    );

                }

            };

            return ga;

        }

        private static GeneticAlgorithm DiagonalExample()

        {

            float maxWidth = 998f;

            float maxHeight = 680f;

​

            var chromosome = new FloatingPointChromosome(

                new double[] { 0, 0, 0, 0 },

                new double[] { maxWidth, maxHeight, maxWidth, maxHeight },

                new int[] { 10, 10, 10, 10 },

                new int[] { 0, 0, 0, 0 });

​

            var population = new Population(50, 100, chromosome);

​

            var fitness = new FuncFitness((c) =>

            {

                var fc = c as FloatingPointChromosome;

​

                var values = fc.ToFloatingPoints();

                var x1 = values[0];

                var y1 = values[1];

                var x2 = values[2];

                var y2 = values[3];

​

                return Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2));

            });

​

            var selection = new EliteSelection();

            var crossover = new UniformCrossover(0.5f);

            var mutation = new FlipBitMutation();

            var termination = new FitnessStagnationTermination(100);

​

            var ga = new GeneticAlgorithm(

                population,

                fitness,

                selection,

                crossover,

                mutation);

​

            ga.Termination = termination;

​

            Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");

​

            var latestFitness = 0.0;

​

            ga.GenerationRan += (sender, e) =>

            {

                var bestChromosome = ga.BestChromosome as FloatingPointChromosome;

                var bestFitness = bestChromosome.Fitness.Value;

​

                if (bestFitness != latestFitness)

                {

                    latestFitness = bestFitness;

                    var phenotype = bestChromosome.ToFloatingPoints();

​

                    Console.WriteLine(

                        "Generation {0,2}: ({1},{2}),({3},{4}) = {5}",

                        ga.GenerationsNumber,

                        phenotype[0],

                        phenotype[1],

                        phenotype[2],

                        phenotype[3],

                        bestFitness

                    );

                }

            };

            return ga;

        }

    }

}