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
See the Documentation for the complete guide to using this library.
Please read the CONTRIBUTING.md file before submitting pull requests.