Mostrar el registro sencillo del ítem

Genetic Programming: From design to improved implementation

hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorLÓPEZ-LÓPEZ, Victor R.
hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorTRUJILLO, Leonardo
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorLEGRAND, Pierrick
hal.structure.identifierCentro de Investigacion Cientifica y de Education Superior de Ensenada [Mexico] [CICESE]
dc.contributor.authorOLAGUE, Gustavo
dc.date.accessioned2024-04-04T02:42:42Z
dc.date.available2024-04-04T02:42:42Z
dc.date.conference2016-06-20
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191266
dc.description.abstractEnGenetic programming (GP) is an evolutionary-based search paradigm that is well suited to automatically solve difficult design problems. The general principles of GP have been used to evolve mathematical functions, models, image operators, programs, and even antennas and lenses. Since GP evolves the syntax and structure of a solution, the evolutionary process can be carried out in one environment and the solution can then be ported to another. However, given the nature of GP it is common that the evolved designs are unorthodox compared to traditional approaches used in the problem domain. Therefore, efficiently porting, improving or optimizing an evolved design might not be a trivial task. In this work we argue that the same GP principles used to evolve the solution can then be used to optimize a particular new implementation of the design, following the Genetic Improvement approach. In particular, this paper presents a case study where evolved image operators are ported from Matlab to OpenCV, and then the source code is optimized an improved using Genetic Improvement of Software for Multiple Objectives (GISMOE). In the example we show that functional behavior is maintained (output image) while improving non-functional properties (computation time). Despite the fact that this first example is a simple case, it clearly illustrates the possibilities of using GP principles in two distinct stages of the software development process, from design to improved implementation.
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enGenetic programming
dc.subject.engenetic improvement
dc.subject.encomputer vision
dc.title.enGenetic Programming: From design to improved implementation
dc.typeCommunication dans un congrès
dc.identifier.doi10.1145/2908961.2931693
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.description.sponsorshipEuropeAnalysis and classification of mental states of vigilance with evolutionary computation
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleGECCO 2016 - Genetic and Evolutionary Computation Conference
bordeaux.countryUS
bordeaux.conference.cityDenver
bordeaux.peerReviewedoui
hal.identifierhal-01389066
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2016-06-24
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01389066v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=L%C3%93PEZ-L%C3%93PEZ,%20Victor%20R.&TRUJILLO,%20Leonardo&LEGRAND,%20Pierrick&OLAGUE,%20Gustavo&rft.genre=unknown


Archivos en el ítem

ArchivosTamañoFormatoVer

No hay archivos asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem