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Bringing evolutionary computation to industrial applications with guide

Authors :
Luis Da Costa
Marc Schoenauer
Machine Learning and Optimisation (TAO)
Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI)
Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec
Laboratoire de Recherche en Informatique (LRI)
Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Microsoft Research - Inria Joint Centre (MSR - INRIA)
Institut National de Recherche en Informatique et en Automatique (Inria)-Microsoft Research Laboratory Cambridge-Microsoft Corporation [Redmond, Wash.]
EvoTest
ACM
Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Source :
GECCO, GECCO 2009, GECCO 2009, ACM, Jul 2009, Montréal, Québec, Canada
Publication Year :
2009
Publisher :
ACM, 2009.

Abstract

International audience; Evolutionary Computation is an exciting research field with the power to assist researchers in the task of solving hard optimization problems (i.e., problems where the exploitable knowledge about the solution space is very hard and/or expensive to obtain). However, Evolutionary Algorithms are rarely used outside the circle of knowledgeable practitioners, and in that way have not achieved a status of useful enough tool to assist "general" researchers. We think that part of the blame is the lack of practical implementations of research efforts reflecting a unifying common ground in the field. In this communication we present GUIDE, a software framework incorporating some of the latest results from the EC research community and offering a Graphical User Interface that allows the straightforward manipulation of evolutionary algorithms. From a high-level description provided by the user it generates the code that is needed to run an evolutionary algorithm in a specified existing library (as of March 2009, EO and ECJ are the possible targeted libraries). GUIDE's GUI allows users to acquire a straightforward understanding of EC ideas, while at the same time providing them with a sophisticated research tool. In this communication we present 3 industrial case studies using GUIDE as one of the main tools in order to perform software testing on large, complex systems.

Details

Database :
OpenAIRE
Journal :
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Accession number :
edsair.doi.dedup.....bb8a666318288b449d2410ebb8f84748