mango-genetic 0.1.0a2

Library with a collection of useful classes and methods to DRY

Mango Genetic

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

pip install mango-genetic

Dependencies

• Python >= 3.10
• numpy >= 1.24.4
• mango[data] == 0.3.0a8

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.