DEAP-ER

DEAP-ER is a complete rewrite of the original DEAP library for Python 3.10 and up, which includes features such as:

• Genetic algorithms using any imaginable containers like:
  • List, Array, Set, Dictionary, Tree, Numpy Array, etc.
• Genetic programming using prefix trees
  • Loosely typed, Strongly typed
  • Automatically defined functions
• Evolution Strategies (Covariance Matrix Adaptation)
• Multi-objective optimisation (SPEA-II, NSGA-II, NSGA-III, MO-CMA)
• Co-evolution (cooperative and competitive) of multiple populations
• Parallelization of evolution processes using multiprocessing or with Ray
• Records to track the evolution and to collect the best individuals
• Checkpoints to persist the progress of evolutions to disk
• Benchmarks to test evolution algorithms against common test functions
• Genealogy of an evolution, that is also compatible with NetworkX
• Examples of alternative algorithms:
  • Symbolic Regression,
  • Particle Swarm Optimization,
  • Differential Evolution,
  • Estimation of Distribution Algorithm

Documentation

See the Documentation for the complete guide to using this library.

Contributing

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