EAP uses both the object oriented and functional programming paradigm in Python to make development simple and beautiful. DTM provides tools to distribute workload evenly on a cluster or LAN of workstations, based on MPI and TCP communication managers.
Date: Jul 7, 2011
We are proud to annouce the release of DEAP 0.7, a library for doing Distributed Evolutionary Algorithms in Python.
You can download a copy of this release at
For those who wouldn't already know about the project, it is built around two major parts, EAP and DTM.
EAP has been built using the Python and UNIX programming philosophies in order to provide a transparent, simple and coherent environment for implementing your favourite evolutionary algorithms. EAP is very easy to use even for those who do not know much about the Python programming language. EAP uses both the object oriented and functional programming paradigm that are provided by Python in order to make development simple and beautiful. It also contains more than 20 illustrative and diversified examples, to help newcomers to ramp up very quickly in using this environment.
The D part of DEAP, called DTM, is under intense development and currently available as an alpha version (0.2). DTM provides tools to distribute workload evenly on a cluster or LAN of workstations, based on MPI and TCP communication managers. The load balancing is based on a new epidemiologic model. This unique model allows unique possibilities, like tasks spawning other tasks that can be run on any available workers.
This release includes a lot of new examples, a cleaner API, new features like easy statistics computation and a benchmark module, new variation methods for finer control on algorithms, and a few bug fixes.
Your feedback and comments are welcome at
deap-users at googlegroups dot com.
You can also follow us on Twitter @deapdev, and on our blog deapdev.wordpress.com/