GeneticSharp

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

Can be used in ASP .NET MVC, Web Forms, Windows Forms, GTK# and Unity3D applications.

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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)
  • Ordered OX1
  • Partially Mapped (PMX)
  • 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

• Terminations

  • Generation number
  • Time evolving
  • Fitness stagnation
  • Fitness threshold
  • And e Or (allows combine others terminations)

• 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 (GTK#) showing the library solving TSP (Travelling Salesman Problem).

  

• Mono support.

  

• Fully tested on Windows and MacOSX.

  

• 100% Unit Tests coveraged.

  

• 100% code documentation.

• FxCop validated.

• Good (and well used) design patterns.

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Usage

Creating your own fitness evaluation

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

Creating your own chromosome

public class YourIChrosomeImplementation : ChromosomeBase
{
	public override Gene GenerateGene (int geneIndex)
	{
		// Generate a gene base on your chromosome representation.
	}

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

Running your GA

var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new YourIFitnessImplementation();
var chromosome = new YourIChrosomeImplementation(); // please, don't use names like that ;)
var population = new Population (50, 70, chromosome);

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

ga.Start();

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Roadmap

• Improve Runner.GtkApp
  • Add new problems/classic samples
    • Checkers
    • Time series
• Create and publish NuGet package
• Create the wiki
• Add new selections
  • Reward-based
• Add new crossovers
  • Order-based (OX2)
  • Position-based (POS)
  • 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
• Unity3d game sample (WIP)
• MonoTouch Runner app (sample)
• Parallel populations (islands)

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FAQ

Having troubles?

• Ask on Stack Overflow

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How to improve it?

Create a fork of GeneticSharp.

Did you change it? Submit a pull request.

License

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.

Change Log

0.5.0 First version.