deap-er 2.0.0

Distributed Evolutionary Algorithms in Python - Entirely Reworked

DEAP-ER

DEAP-ER is a complete rework and refactor of the original DEAP evolutionary computation framework library for Python 3.9, 3.10 and up. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelization mechanisms such as multiprocessing and Ray.

DEAP includes the following features:

• Genetic algorithm using any imaginable representation
  • List, Array, Set, Dictionary, Tree, Numpy Array, etc.
• Genetic programming using prefix trees
  • Loosely typed, Strongly typed
  • Automatically defined functions
• Evolution strategies (including CMA-ES)
• Multi-objective optimisation (NSGA-II, NSGA-III, SPEA2, MO-CMA-ES)
• Co-evolution (cooperative and competitive) of multiple populations
• Parallelization of the evaluations (and more)
• Hall of Fame of the best individuals that lived in the population
• Checkpoints that take snapshots of a system regularly
• Benchmarks module containing most common test functions
• Genealogy of an evolution, that is also compatible with NetworkX
• Examples of alternative algorithms : Particle Swarm Optimization, Differential Evolution, Estimation of Distribution Algorithm

Documentation

See the Documentation for the user guide, tutorials and the reference manual.

Installation

pip install deap-er

Importing

from deap_er import base, creator, tools, env, gp

Contributing

Please read the CONTRIBUTING.md file before submitting pull requests.