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.

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