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 Core and .NET Framework apps, like ASP .NET MVC, ASP .NET Core, Web Forms, UWP, Windows Forms, GTK#, Xamarin and Unity3D games.

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Projects, papers, journals, books, tutorials, courses and apps using GeneticSharp

• AeroVision: aircraft trajectories optimization and visualization (paper)
• Artificial Intelligence course in French engineering schools (course)
• Analysis and comparison between Black-Scholes and Merton and Corrado-Su for options pricing (paper) 🇧🇷
• Context-Sensitive Code Completion: improving predictions with genetic algorithms (paper) (Github)
• Deriving Functions for Pareto Optimal Fronts Using Genetic Programming (paper/book)
• Designing and creating a self managing distributed file system (paper)
• Developing trading strategies with genetic algorithms (forum) (paper)
• Frixel: 2D framing / pixel structural optimization (project)
• Function optimization with GeneticSharp (tutorial)
• GeneticSharp Car2D (sample)
• GeneticSharp Unity3d Samples (Android app)
• Genetic Scheduler: a genetic algorithm for scheduling tasks with temporal restriction in distributed systems (paper) 🇧🇷
• Lean Optimization: genetic optimization using LEAN (GitHub)
• OpenFMSL: simulate stationary chemical processes using mass and energy balances (project)
• Optimization of patient flow in emergency departments using Genetic Algorithms (paper)
• Overload journal 142: Evolutionary computing frameworks for optimisation (journal)
• Path Finding with Genetic Algorithms (project)
• SurvivorAI: some experiments of survival scenarios (project)
• ThermalDesign (project)
• TSP with GeneticSharp and Unity3D (tutorial)
• VetMedData.NET: a .NET Core class library for obtaining structured data on UK-licensed Veterinary Medicines (project)
• Are you using GeneticSharp in your project? Please, let me know!

Features

Chromosomes

• FloatingPointChromosome
• IntegerChromosome
• Add your own chromosome representation implementing IChromosome / IBinaryChromosome interfaces or extending ChromosomeBase / BinaryChromosomeBase.

Fitness

Add your own fitness evaluation, implementing IFitness interface.

Populations

• Population
• TplPopulation
• Others populations can be added implementing IPopulation interface or extending Population.

Generations

• Generation
• Generation strategy
  • Performance strategy
  • Tracking strategy
  • Others generation strategies can be added implementing IGenerationStrategy.cs interface.

Operators strategy

• DefaultOperatorsStrategy
• TplOperatorsStrategy
• Others operators strategies can be added implementing IOperatorsStrategy interface.

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

• Alternating-position (AP)
• 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
• Voting Recombination (VR)
• Others crossovers can be added implementing ICrossover interface or extending CrossoverBase.

Mutations

• Displacement
• Flip Bit
• Insertion
• Partial Shuffle (PSM)
• Reverse Sequence (RSM)
• Twors
• Uniform
• Others mutations can be added implementing IMutation interface or extending MutationBase / SequenceMutationBase.

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.

Task executors

• LinearTaskExecutor
• ParallelTaskExecutor
• TplTaskExecutor
• Others task executors can be added implementing ITaskExecutor interface.

Samples

Console samples

• AutoConfig
• Bitmap equality
• Equality equation
• Equation solver
• Function builder
• Ghostwriter
• TSP (Travelling Salesman Problem)

GTK# samples

• Bitmap equality
• Function optimization
• Sudoku
• TSP (Travelling Salesman Problem)

Unity3D Samples

• Car2D
• TSP (Travelling Salesman Problem)
• Wall Builder

Multi-platform

• Mono, .NET Standard 2.0 and .NET Framework 4.6.2 support
• Fully tested on Windows and MacOS

Code quality

• 100% unit test code coverage
• 100% code documentation
• SonarCloud validated
• Performance benchmarks

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Setup

.NET Standard 2.0 and .NET Framework 4.6.2

Only GeneticSharp:

install-package GeneticSharp

GeneticSharp and extensions (TSP, AutoConfig, Bitmap equality, Equality equation, Equation solver, Function builder, etc):

install-package GeneticSharp.Extensions

Unity3D

If want to use GeneticSharp on Unity3D you can use the latest GeneticSharp.unitypackage available on our release page.

Mono and .NET Framework 3.5

To install previous version that support .NET Framework 3.5:

install-package GeneticSharp -Version 1.2.0

Running samples

If you want to run the console, GTK# and Unity samples, just fork this repository and follow the instruction from our setup page wiki.

An easy way to run the Unity Samples, if you have a Android device, is download it from Google Play.

Usage

Creating your own fitness evaluation

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

Creating your own 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();
	}
}

Running your GA

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);

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FAQ

Having troubles?

• Read our wiki.
• Tutorials
  • Function optimization with GeneticSharp
  • TSP with GeneticSharp and Unity3D
• Ask on Stack Overflow using the tag GeneticSharp.
• Open an issue.

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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, etc.

Thanks to

• AppVeyor: open source license for continuous integration.
• JetBrains: open source license for all products pack.
• SMASHINGLOGO: GeneticSharp's logo.
• SonarCloud: open source license for online inspection.