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.

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

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

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

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.

Samples

Console sample

AutoConfig
Bitmap equality
Equality equation
Equation solver
Function builder

Ghostwriter
TSP (Travelling Salesman Problem)

GTK# sample

TSP (Travelling Salesman Problem), Function optimization and Sudoku

Bitmap equality

Unity3D Samples

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

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.

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);

Roadmap

Add new problems/classic sample
- Checkers
- Time series
- Knapsack problem
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
- Boundary
- Gaussian
Add new terminations
- Fitness convergence
- Population convergence
- Chromosome convergence
New samples
- Xamarin runner app (sample)
Parallel populations (islands)

FAQ

Having troubles?

Read our wiki.
Tutorials
- Function optimization with GeneticSharp
- TSP with GeneticSharp and Unity3D
Ask on Twitter @ogiacomelli.
Ask on Stack Overflow using the tag GeneticSharp.

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.

Thanks to

I would like to thanks to the guys from SMASHINGLOGO (https://smashinglogo.com) for the amazing GeneticSharp logo.

floydpink / geneticsharp Goto Github PK

geneticsharp's Introduction