Implementation And Usage

 1using System;
 2using FinMath.MachineLearning.EvolutionaryAlgorithms;
 3using FinMath.Statistics;
 4
 5namespace FinMath.Samples
 6{
 7    class GeneticAlgo
 8    {
 9        // Chromosomes for this task represents two dimensional Rastrigin function arguments.
10        public class RastriginArguments
11        {
12            public double x, y;
13        }
14
15        // Domain boundaries.
16        static double Min, Max;
17
18        static void Main()
19        {
20            const Int32 stepCount = 25;
21            const Int32 populationSize = 50000;
22            Min = -10;
23            Max = 10;
24
25            // Initialize new genetic algorithm class instance.
26            GeneticAlgorithm<RastriginArguments> ga = new GeneticAlgorithm<RastriginArguments>(Creation, Crossover, Mutation, RastriginFunction);
27
28            // Set GA to make only one generation.
29            ga.PopulationSize = populationSize;
30
31            // Perform one step of Genetic Algorithm.
32            // You can set generation count to anything you want and call "Run" method to make arbitrary number of steps.
33            ga.GenerationsCount = 1;
34            ga.Run();
35            for (Int32 i = 0; i < stepCount; ++i)
36            {
37                // Continue optimization process.
38                ga.Continue();
39                // Get best solution candidate.
40                RastriginArguments best = ga.Population[0];
41
42                // Output the coordinates of minimum of the function.
43                Console.WriteLine($"{i:00}: x = {best.x:0.00000}, y = {best.y:0.00000} value = {ga.BestObjective:0.00000}");
44            }
45        }
46
47+Rastrigin routines47-        #region Rastrigin routines
48 
49         // Return Rastrigin function individual.
50         public static RastriginArguments Creation(RandomGenerator r)
51         {
52             double tx = r.NextDouble(Min, Max);
53             double ty = r.NextDouble(Min, Max);
54 
55             return new RastriginArguments { x = tx, y = ty };
56         }
57 
58         // Perform a crossover (child X = parents X coordinates sum divided by 2).
59         public static void Crossover(RastriginArguments mother, RastriginArguments father, ref RastriginArguments child, RandomGenerator r)
60         {
61             child.x = (mother.x + father.x) / 2;
62         }
63 
64         // Mutate this individual (value of x coordinate is selected randomly).
65         public static void Mutation(ref RastriginArguments t, RandomGenerator r)
66         {
67             if (r.Next(2) == 0)
68                 t.x = r.NextDouble(Min, Max);
69             else
70                 t.y = r.NextDouble(Min, Max);
71         }
72 
73         // Fitness is the Rastrigin function value.
74         public static double RastriginFunction(RastriginArguments t)
75         {
76             Double x = t.x;
77             Double y = t.y;
78 
79             return 20 + Math.Pow(x, 2) + Math.Pow(y, 2) - 10 * (Math.Cos(2 * Math.PI * x) + Math.Cos(2 * Math.PI * y));
80         }
81         #endregion
82    }
83}