6393Re: [GP] Distributed Evolutionary Algorithm in Python (DEAP) Turns 1.0
- Feb 22, 2014Hello, ECers and GPers!Congratulations to DEAP folks and thanks a lot for that!Please, any news about the following issue? :"We are pleased to announce that, after GECCO 2013, we plan to publish a special issue of the journal Genetic Programming and Evolvable Machines, which will showcase the top entries from each competition. The authors of the winners will be asked to write a paper describing their methods and results. The papers will undergo review and be published in the journal (subject to reviewing results). We hope to make this a very special issue, highlighting the accomplishments of EC in challenging problems."http://www.geccocompetitions.com/2013/05/05/112/Cheers,
Douglas Mota Dias, DScPostdoctoral ResearcherICA: Applied Computational Intelligence LaboratoryDepartment of Electrical EngineeringPontifical Catholic University of Rio de Janeiro - PUC-RioBrazil
Em Sábado, 22 de Fevereiro de 2014 9:36, w langdon <W.Langdon@...> escreveu:
The latest version of SIGEvolution,
the newsletter of the ACM, appears to be available:
http://www.sigevolution.org/ (or GP bibliography;-)
Evolutionary Game Design by Cameron Browne
GECCO-2013 competition report
calls & calendar
as well as Francois-Michel's article on DEAP
Dr. W. B. Langdon,
Department of Computer Science,
University College London
Gower Street, London WC1E 6BT, UK
GECCO 2014 http://www.sigevo.org/gecco-2014/
EuroGP 2014 http://www.evostar.org/cfpEuroGP.html
choose your background
A Field Guide to Genetic Programming
GP EM http://www.springer.com/10710
GP Bibliography http://www.cs.bham.ac.uk/~wbl/biblio/
On 2/20/14, f.derainville <f.derainville@...> wrote:
> Dear ECers,
> After more than 4 years of development, we are proud to announce the
> release of DEAP 1.0.0. You can download a copy of this release at the
> following web page.
> DEAP (Distributed Evolutionary Algorithms in Python) is a novel evolutionary
> computation framework for rapid prototyping and testing of ideas. Its design
> departs from most other existing frameworks in that it seeks to make
> explicit and data structures transparent, as opposed to the more common
> box type of frameworks.
> To get to know more about DEAP and the current release, we invite you
> to read the most recent article on DEAP published in SIGEvolution volume 6,
> issue 2, pp. 17-26.
> An IPython notebook version of the article is also available.
> This release includes:
> - Major overhaul of statistics computing and logging;
> - Ability to do Object Oriented Genetic Programming (OOGP);
> - Symbolic regression benchmarks for GP;
> - New tutorials and better documentation;
> - Several new examples from diverse fields;
> - and several other changes.
> Every changes of this release are detailed in the documentation.
> To help users translate code from 0.9.x to 1.0.0, we have also written
> a new porting guide that details every change required to use DEAP 1.0.
> Your feedback and comments are welcome at http://goo.gl/LZkdi4 or
> deap-users at googlegroups dot com. You can also follow us on Twitter
> and on our blog http://deapdev.wordpress.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
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