{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"DEAP is an evolutionary computation framework for implementing genetic algorithms, genetic programming, evolution strategies, and multi-objective optimization with support for arbitrary data structures and parallelization.","skillfed_tags":["optimization","evolutionary-algorithms","parallelizable"],"use_cases":["Implement a genetic algorithm to optimize parameters for a machine learning model or engineering design","Solve multi-objective optimization problems where multiple competing objectives must be balanced","Develop genetic programming solutions to evolve symbolic expressions or program trees for regression or classification","Run evolution strategies like CMA-ES for continuous optimization of high-dimensional problems","Prototype particle swarm optimization or differential evolution algorithms for comparison studies"],"what_it_does":"DEAP is a framework for building evolutionary algorithms in Python. It provides building blocks for genetic algorithms, genetic programming, evolution strategies (including CMA-ES), and multi-objective optimization (NSGA-II, NSGA-III, SPEA2, MO-CMA-ES). The framework emphasizes making algorithms explicit and data structures transparent, allowing you to work with any representation\u2014lists, arrays, sets, dictionaries, trees, or numpy arrays. It integrates with numpy and moocore for numerical operations and multi-objective tasks.\n\nThe package is designed for rapid prototyping and testing of evolutionary ideas. It includes utilities like a Hall of Fame for tracking the best individuals, checkpoints for system snapshots, benchmarks with common test functions, and genealogy tracking compatible with NetworkX. DEAP supports parallelization of fitness evaluations through mechanisms like multiprocessing and SCOOP, making it suitable for computationally intensive optimization problems.","worth_installing":"Yes. DEAP is actively maintained, has low install friction, carries no known vulnerabilities, and is widely used in research and industry for evolutionary computation. The LGPL license is standard for research software and poses no barrier for most use cases. Install it if you need a mature, flexible framework for genetic algorithms, genetic programming, or multi-objective optimization."},"id":"deap","links":{"html":"https://skillfed.io/packages/deap","md":"https://skillfed.io/packages/deap.md","pypi":"https://pypi.org/project/deap/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-17","license_spdx":null,"license_treatment":"copyleft","name":"deap","python_support":"unspecified","summary":"Distributed Evolutionary Algorithms in Python"},"popularity":{"monthly_downloads":500364,"position":6321,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.4.4"}