https://github.com/okabe-junya/gago

is a simple genetic algorithm library written in go
https://github.com/okabe-junya/gago

genetic-algorithm golang

Last synced: about 1 month ago
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is a simple genetic algorithm library written in go

• Host: GitHub
• URL: https://github.com/okabe-junya/gago
• Owner: Okabe-Junya
• License: mit
• Created: 2024-07-20T18:01:46.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-21T14:25:34.000Z (7 months ago)
• Last Synced: 2024-11-09T11:08:44.922Z (3 months ago)
• Topics: genetic-algorithm, golang
• Language: Go
• Homepage:
• Size: 26.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERS

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# GAGO: Simple Genetic Algorithm Library written in Go

[![Test](https://github.com/Okabe-Junya/gago/actions/workflows/test.yml/badge.svg)](https://github.com/Okabe-Junya/gago/actions/workflows/test.yml) [![CodeQL](https://github.com/Okabe-Junya/gago/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/Okabe-Junya/gago/actions/workflows/github-code-scanning/codeql) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Go Report Card](https://goreportcard.com/badge/github.com/Okabe-Junya/gago)](https://goreportcard.com/report/github.com/Okabe-Junya/gago) [![Go Reference](https://pkg.go.dev/badge/github.com/Okabe-Junya/gago.svg)](https://pkg.go.dev/github.com/Okabe-Junya/gago)

## Overview

**GAGO** is a go library for implementing genetic algorithms. The library is designed to be flexible and extensible, allowing users to define their own selection, crossover, and mutation functions.

## Installation

```bash

go get -u github.com/Okabe-Junya/gago

```

## Usage

See the [examples](./examples/) directory for more details. The following is a simple example of how to use the library.

> [!NOTE]

> You need to define some functions to use this example.

```go

func main() {

    gaInstance := &ga.GA{

        Selection:     func(population []*ga.Individual) []*ga.Individual { return ga.TournamentSelection(population, 3) },

        Crossover:     ga.SinglePointCrossover,

        Mutation:      ga.BitFlipMutation,

        CrossoverRate: crossoverRate,

        MutationRate:  mutationRate,

        Generations:   generations,

        EnableLogger:  true,

    }

    gaInstance.Initialize(populationSize, initializeGenotype, evaluatePhenotype)

    gaInstance.Evolve(evaluatePhenotype)

    bestIndividual := findBestIndividual(gaInstance.Population)

    bestX := decodeGenotype(bestIndividual.Genotype)

    fmt.Printf("Best x: %f, Fitness: %f\n", bestX, bestIndividual.Phenotype.Fitness)

}

```

## License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.