Function optimization with GeneticSharp: http://diegogiacomelli.com.br/function-optimization-with-geneticsharp/

CompleteSourceCode.cs

using System;
using GeneticSharp.Domain;
using GeneticSharp.Domain.Chromosomes;
using GeneticSharp.Domain.Crossovers;
using GeneticSharp.Domain.Fitnesses;
using GeneticSharp.Domain.Mutations;
using GeneticSharp.Domain.Populations;
using GeneticSharp.Domain.Selections;
using GeneticSharp.Domain.Terminations;

namespace FunctionOptimizationWithGeneticSharp
{
	class MainClass
	{
		public static void Main(string[] args)
		{
			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
					);
				}
			};

			ga.Start();

			Console.ReadKey();
		}
	}
}

CreatingAnEuclideanDistanceChromsome.cs

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 });

CreatingTheCrossover.cs

var crossover = new UniformCrossover(0.5f);

CreatingTheFitnessFunction.cs

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));
});

CreatingTheMutation.cs

var mutation = new FlipBitMutation();

CreatingThePopulation.cs

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

CreatingTheSelection.cs

var selection = new EliteSelection();

CreatingTheTermination.cs

var termination = new FitnessStagnationTermination(100);

RunningtheGA1.cs

var ga = new GeneticAlgorithm(
	population,
	fitness,
	selection,
	crossover,
	mutation);

ga.Termination = termination;

ga.Start();

RunningtheGA2.cs

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
		);
	}
};

ga.Start();

Console.ReadKey();