SimpleML.GeneticAlgorithm

A simple, extensible Genetic Algorithm implementation for .NET 8.

Install

dotnet add package SimpleML.GeneticAlgorithm

Usage

// Implement IFitnessFunction for your problem
public class MyFitness : IFitnessFunction
{
    public double Evaluate(Genotype genotype) => /* compute fitness */;
    public Task<double> EvaluateAsync(Genotype genotype) => Task.FromResult(Evaluate(genotype));
}

// Configure and run
var settings = new GeneticAlgorithmSettings(new MyFitness(), problemSize: 100)
{
    PopulationSize = 5000,
    SurvivalRate = 0.1,
    MutationRate = 0.05,
    StopFunction = new BasicStopFunction { MaxEpochs = 500, MinFitness = 95 }
};

var ga = new GeneticAlgorithm(settings);
await ga.Run();

Console.WriteLine($"Best fitness: {ga.RunInfo.BestFitnessSoFar}");

Key types

Type	Description
GeneticAlgorithm	Orchestrates the evolution loop
GeneticAlgorithmSettings	Population size, survival/mutation rates, stop condition
Population	Gene pool management, selection, and breeding
Genotype	Individual solution encoded as a boolean array
BasicStopFunction	Stops after max epochs, target fitness, or fitness plateau
EliteSelection	Keeps the top N organisms
BinaryTournamentSelection	Random pairwise tournament selection

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The idea of the project is to implement various popular ML algorithms in a simple yet efficient way, conforming to best patterns and practices of modern OO programming. PRs are very welcome.