Evolutionary Algorithms in Python (EAP) First Public Release (0.6)
felix.antoine.fortin at gmail.com
Fri Oct 8 00:41:20 CEST 2010
We are proud to announce the first public release of EAP, a library
for doing Evolutionary Algorithms in Python. You can download a copy
of this open source project at the following web page.
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 the object oriented paradigm that is
provided by Python in order to make development simple and beautiful.
It also contains a 15 illustrative and diversified examples, to help
newcomers to ramp up very quickly in using this environment.
EAP is part of the DEAP project, that also includes some facilities
for the automatic distribution and parallelization of tasks over a
cluster of computers. The D part of DEAP, called DTM, is under intense
development and currently available as an alpha version. DTM currently
provides two and a half ways to distribute workload on a cluster or
LAN of workstations, based on MPI and TCP communication managers.
This public release (version 0.6) is more complete and simpler than
ever. It includes Genetic Algorithms using any imaginable
representation, Genetic Programming with strongly and loosely typed
trees in addition to automatically defined functions, Evolution
Strategies (including Covariance Matrix Adaptation), multiobjective
optimization techniques (NSGA-II and SPEA2), easy parallelization of
algorithms and much more like milestones, genealogy, etc.
We are impatient to hear your feedback and comments on that system at
<deap-users at googlegroups dot com>
François-Michel De Rainville
Laboratoire de Vision et Systèmes Numériques
Département de génie électrique et génie informatique
Quebec City (Quebec), Canada
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