####Build

####Code quality

####Release

GeneticSharp is a fast, extensible, multi-platform and multithreading C# Genetic Algorithm library that simplifies the development of applications using Genetic Algorithms (GAs).

Can be used in any kind of .NET apps, like ASP .NET MVC, Web Forms, Windows Forms, GTK# and Unity3D applications.

##Projects and papers using GeneticSharp

• AeroVision: aircraft trajectories optimization and visualization
• Designing and creating a self managing distributed file system
• Path Finding with Genetic Algorithms
• SurvivorAI: some experiments of survival scenarios
• Are you using GeneticSharp in your project? Please, let me know!

##Features

###Chromosomes Add your own chromosome representation implementing IChromosome interface or extending ChromosomeBase

###Fitness Add your own fitness evaluation, implementing IFitness interface.

###Populations

• Generation
• Generation strategy
  • Performance strategy
  • Tracking strategy

###Selections

• Elite (also know as Truncate or Truncation)
• Roulette Wheel
• Stochastic Universal Sampling
• Tournament
• Others selections can be added implementing ISelection interface or extending SelectionBase.

###Crossovers

• Cut and Splice
• Cycle (CX)
• One-Point (C1)
• Order-based (OX2)
• Ordered (OX1)
• Partially Mapped (PMX)
• Position-based (POS)
• Three parent
• Two-Point (C2)
• Uniform
• Others crossovers can be added implementing ICrossover interface or extending CrossoverBase.

###Mutations

• Reverse Sequence (RSM)
• Twors
• Uniform
• Others mutations can be added implementing IMutation interface or extending MutationBase.

###Reinsertions

• Elitist
• Fitness Based
• Pure
• Uniform
• Others reinsertions can be added implementing IReinsertion interface or extending ReinsertionBase.

###Terminations

• Generation number
• Time evolving
• Fitness stagnation
• Fitness threshold
• And e Or (allows combine others terminations)
• Others terminations can be added implementing ITermination interface or extending TerminationBase.

###Randomizations

• Basic randomization (using System.Random)
• Fast random
• If you need a special kind of randomization for your GA, just implement the IRandomization interface.

###Runner app (console) with samples

• AutoConfig

• Bitmap equality

• Equality equation

• Equation solver

• Function builder

  

• Ghostwriter

• TSP (Travelling Salesman Problem)

###Runner app (GTK#) with visual samples:

####TSP (Travelling Salesman Problem).

####Bitmap equality

###Multi-platform

• Mono support.
• Fully tested on Windows and MacOSX.

###Code quality

• 100% unit test code coverage.
• FxCop validated.
• Code duplicated verification.
• Good (and well used) design patterns.
• 100% code documentation.

PM> Install-Package GeneticSharp

public class MyProblemFitness : IFitness
{  
	public double Evaluate (IChromosome chromosome)
	{
		// Evaluate the fitness of chromosome.
	}
}

public class MyProblemChromosome : ChromosomeBase
{
    // Change the argument value passed to base construtor to change the length 
 	// of your chromosome.
 	public MyProblemChromosome() : base(10) 
    {
   		CreateGenes();
    }

	public override Gene GenerateGene (int geneIndex)
	{
		// Generate a gene base on my problem chromosome representation.
	}

	public override IChromosome CreateNew ()
	{
		return new MyProblemChromosome();
	}
}

var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome();
var population = new Population (50, 70, chromosome);

var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);

Console.WriteLine("GA running...");
ga.Start();

Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);

• Unity3d game sample (WIP)
• Improve Runner.GtkApp
  • Add new problems/classic samples
    • Checkers
    • Time series
    • Knapsack problem
• Create the wiki
• Add new selections
  • Reward-based
• Add new crossovers
  • Voting recombination
  • Alternating-position (AP)
  • Sequential Constructive (SCX)
  • Shuffle crossover
  • Precedence Preservative Crossover (PPX)
• Add new mutations
  • Non-Uniform
  • Flip Bit
  • Boundary
  • Gaussian
• Add new terminations
  • Fitness convergence
  • Population convergence
  • Chromosome convergence
• MonoTouch Runner app (sample)
• Parallel populations (islands)

Having troubles?

• Read our wiki.
• Ask on Twitter @ogiacomelli.
• Ask on Stack Overflow using the tag GeneticSharp.

Create a fork of GeneticSharp.

Did you change it? Submit a pull request.

Licensed under the The MIT License (MIT). In others words, you can use this library for developement any kind of software: open source, commercial, proprietary and alien.