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24 lines
1.1 KiB
Plaintext
24 lines
1.1 KiB
Plaintext
DEAP is a novel evolutionary computation framework for rapid prototyping and
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testing of ideas. It seeks to make algorithms explicit and data structures
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transparent. It works in perfect harmony with parallelisation mechanisms such
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as multiprocessing and SCOOP.
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DEAP includes the following features:
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* Genetic algorithm using any imaginable representation
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o List, Array, Set, Dictionary, Tree, Numpy Array, etc.
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* Genetic programing using prefix trees
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o Loosely typed, Strongly typed
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o Automatically defined functions
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* Evolution strategies (including CMA-ES)
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* Multi-objective optimisation (NSGA-II, SPEA2, MO-CMA-ES)
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* Co-evolution (cooperative and competitive) of multiple populations
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* Parallelization of the evaluations (and more)
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* Hall of Fame of the best individuals that lived in the population
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* Checkpoints that take snapshots of a system regularly
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* Benchmarks module containing most common test functions
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* Genealogy of an evolution (that is compatible with NetworkX)
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* Examples of alternative algorithms : Particle Swarm Optimization, Differential
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Evolution, Estimation of Distribution Algorithm
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WWW: https://github.com/DEAP/deap
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