Archive for the ‘deap’ Category

Basic PSO optimization with DEAP

Following a question we had regarding if we thought about integrating swarm intelligence algorithm in DEAP, we thought : what does it take to implement the original particle swarm optimization (PSO) algorithm in EAP? The answer was : everything is already implemented!

So we rushed to code, and built a simple example, that optimizes a function H1 described in  “The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems”, by A. J. Knoek van Soest and L. J. R. Richard Casius. The example and the explanations on how to implement PSO with DEAP are described on googlecode wiki : PSOExample.

We also posted a video of one run plotted with matplotlib on Youtube.

Have fun with DEAP!

Categories: deap, example, new feature

Evolutionary Algorithms in Python (EAP) First Public Release

Hi everyone, 

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
Félix-Antoine Fortin
Marc-André Gardner
Christian Gagné
Marc Parizeau

Laboratoire de vision et systèmes numériques
Département de génie électrique et génie informatique
Université Laval
Quebec City (Quebec), Canada

Categories: deap, release