DEAP is a novel evolutionary computation framework for rapid prototyping and
testing of ideas. It seeks to make algorithms explicit and data structures
transparent. It works in perfect harmony with parallelisation mechanisms such
as multiprocessing and SCOOP.

DEAP includes the following features:
* Genetic algorithm using any imaginable representation
  o List, Array, Set, Dictionary, Tree, Numpy Array, etc.
* Genetic programing using prefix trees
  o  Loosely typed, Strongly typed
  o  Automatically defined functions
* Evolution strategies (including CMA-ES)
* Multi-objective optimisation (NSGA-II, 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 compatible with NetworkX)
* Examples of alternative algorithms : Particle Swarm Optimization, Differential
  Evolution, Estimation of Distribution Algorithm