mango-genetic 1.0.3a1

Library with a collection of useful classes and methods to DRY

A Python library for implementing genetic algorithms and evolutionary computation methods.

Overview

Mango Genetic provides a comprehensive framework for building and running genetic algorithms. It includes implementations of various genetic operators such as selection, crossover, mutation, and replacement strategies.

Features

Individual Management - Base classes for representing individuals with different encoding types (real, binary, integer, categorical)

Population Control - Population management with configurable size and generation limits

Selection Methods - Multiple selection strategies including roulette wheel, tournament, rank-based, and elitism

Crossover Operators - Various crossover methods like blend, one-split, two-split, linear, flat, gaussian, and mask

Mutation Control - Configurable mutation rates with static, adaptive, gene-based, and population-based approaches

Replacement Strategies - Different replacement methods including elitist, stochastic elitist, random, and offspring-only

Configuration System - Flexible configuration management for all genetic algorithm parameters

Installation

Using uv:

uv add mango-genetic

Using pip:

pip install mango-genetic

Dependencies

• Python >= 3.10

• numpy >= 1.24.4

• mango[data] == 1.0.2

Quick Start

from mango_genetic.config import GeneticBaseConfig
from mango_genetic.individual import Individual
from mango_genetic.population import Population

# Load configuration
config = GeneticBaseConfig("config.cfg")

# Create population and run genetic algorithm
population = Population(config, fitness_function)
population.run()

Documentation

For detailed documentation, visit the Mango Documentation.

License

This project is licensed under the Apache Software License.

Support

For questions, issues, or contributions, please contact:

• Email: [e-mail]

• Create an issue on the repository

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