DEAP is a project intended to be an easy to use distributed evolutionary algorithm library in the Python language. Its two main components are modular and can be used separately. The first module is a Distributed Task Manager (DTM), which is intended to run on cluster of computers. The second part is the Evolutionary Algorithms in Python (EAP) framework.
DTM
DTM is a distributed task manager that is able to spread work load over a buch of computer using a TCP or a MPI connection.
DTM include the following features:
* Easy to use parallelization paradigms
* Offers a similar interface to the multiprocessing module
* Basic load balancing algorithm
* Works with both mpi4py and pyMPI
* Support for TCP communication manager
EAP
Features:
EAP includes the following features:
* Genetic algorithm using any imaginable representation
- List, Array, Set, Dictionary, Tree, ...
* Genetic programing using prefix trees
- Loosely typed, Strongly typed
- Automatically defined functions (new v0.6)
* Evolution strategies (including CMA-ES)
* Multi-objective optimisation (NSGA-II, SPEA-II)
* Parallelization of the evaluations (and maybe more) (requires python2.6 and preferably python2.7) (new v0.6)
* Genealogy of an evolution (that is compatible with NetworkX) (new v0.6)
* Hall of Fame of the best individuals that lived in the population (new v0.5)
* Milestones that take snapshot of a system regularly (new v0.5)
Product's homepage
Requirements:
· Python
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