Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms
Identifieur interne : 002427 ( Hal/Checkpoint ); précédent : 002426; suivant : 002428Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms
Auteurs : Jean-Charles Quinton [France]Source :
Abstract
The Continuous Neural Field Theory introduces biologically-inspired competition mechanisms in computational models of perception and action. This paper deals with the use of Genetic Algorithms to optimize its parameters, as to guarantee the emergence of robust cognitive properties. Such properties include the tracking of initially salient stimuli despite strong noise and distracters. Interactions between the parameter values, input dynamics and accuracy of model, as well as their implications for Genetic Algorithms are discussed. The fitness function and set of scenarios used to evaluate the parameters through simulation must be carefully chosen. Experimental results reflect an ineluctable tradeoff between generality and performance.
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biologically-inspired competition mechanisms in computational models of perception and action. This paper deals with the use of Genetic Algorithms to optimize its parameters, as to guarantee the emergence of robust cognitive properties. Such properties include the tracking of initially salient stimuli despite strong noise and distracters. Interactions between the parameter values, input dynamics and accuracy of model, as well as their implications for Genetic Algorithms are discussed. The fitness function and set of scenarios used to evaluate the parameters through simulation must be carefully chosen. Experimental results reflect an ineluctable tradeoff between generality and performance.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms</title><author role="aut"><persName><forename type="first">Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email><idno type="halauthor">498336</idno><affiliation ref="#struct-2348"></affiliation></author><editor role="depositor"><persName><forename>Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2010-06-03 13:56:12</date><date type="whenModified">2016-05-19 01:09:23</date><date type="whenReleased">2010-06-03 20:13:07</date><date type="whenProduced">2010-07-18</date><date type="whenEndEmbargoed">2010-06-03</date><ref type="file" target="https://hal.inria.fr/inria-00488914/document"><date notBefore="2010-06-03"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00488914/file/ag_cnft_IEEE_Xplore_compatible_PID1281655.pdf"><date notBefore="2010-06-03"></date></ref></edition><respStmt><resp>contributor</resp><name key="149687"><persName><forename>Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00488914</idno><idno type="halUri">https://hal.inria.fr/inria-00488914</idno><idno type="halBibtex">quinton:inria-00488914</idno><idno type="halRefHtml">IEEE World Congress on Computational Intelligence 2010 - WCCI 2010, Jul 2010, Barcelona, Spain. 2010</idno><idno type="halRef">IEEE World Congress on Computational Intelligence 2010 - WCCI 2010, Jul 2010, Barcelona, Spain. 2010</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms</title><author role="aut"><persName><forename type="first">Jean-Charles</forename><surname>Quinton</surname></persName><email>quintonj@loria.fr</email><idno type="halAuthorId">498336</idno><affiliation ref="#struct-2348"></affiliation></author></analytic><monogr><meeting><title>IEEE World Congress on Computational Intelligence 2010 - WCCI 2010</title><date type="start">2010-07-18</date><date type="end">2010-07-23</date><settlement>Barcelona</settlement><country key="ES">Spain</country></meeting><imprint><date type="datePub">2010-07-18</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="scco.comp">Cognitive science/Computer science</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">The Continuous Neural Field Theory introduces biologically-inspired competition mechanisms in computational models of perception and action. This paper deals with the use of Genetic Algorithms to optimize its parameters, as to guarantee the emergence of robust cognitive properties. Such properties include the tracking of initially salient stimuli despite strong noise and distracters. Interactions between the parameter values, input dynamics and accuracy of model, as well as their implications for Genetic Algorithms are discussed. The fitness function and set of scenarios used to evaluate the parameters through simulation must be carefully chosen. Experimental results reflect an ineluctable tradeoff between generality and performance.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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