DEAP: Distributed Evolutionary Algorithms in Python
From the website:
DEAP is 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.
Components
DTM
DTM is a distributed task manager that is able to spread workload over a buch of computers using a TCP or a MPI connection.
DTM include the following features:
- Easy to use parallelization paradigms
- Offers a similar interface to the Python's multiprocessing module
- Basic load balancing algorithm
- Works over mpi4py
- Support for TCP communication manager
EAP
EAP is the evolutionary core of DEAP, it provides data structures, methods and tools to design any kind of evolutionary algorithm. It works in perfect harmony with DTM, allowing easy parallelization of any demanding evolutionary task.
EAP includes the following features:
- Genetic algorithm using any imaginable representation
- List, Array, Set, Dictionary, Tree, Numpy Array (tip revision), etc.
- Genetic programing using prefix trees
- Loosely typed, Strongly typed
- Automatically defined functions
- Evolution strategies (including CMA-ES)
- Multi-objective optimisation (NSGA-II, SPEA-II)
- Co-evolution 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
If you are interested in evolutionary approaches to data mining, not a bad place to start.