Generalization capabilities of co-evolution in learning robot behavior

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dc.contributor.author Berlanga de Jesús, Antonio
dc.contributor.author Sanchis de Miguel, María Araceli
dc.contributor.author Isasi, Pedro
dc.contributor.author Molina López, José Manuel
dc.date.accessioned 2009-11-27T12:41:11Z
dc.date.available 2009-11-27T12:41:11Z
dc.date.issued 2002-09-18
dc.identifier.bibliographicCitation Journal of Robotic Systems, 2002, vol. 19, n. 10, p. 455-467
dc.identifier.issn 0741-2223
dc.identifier.uri http://hdl.handle.net/10016/5829
dc.description.abstract In this article, a co-evolutive method is used to evolve neural controllers for general obstacle-avoidance of a Braitenberg vehicle. During a first evolutionary process, Evolution Strategies were applied to generate neural controllers; the generality of the obtained behaviors was quite poor. During a second evolutionary process, a new co-evolutive method, called Uniform Co-evolution, is introduced to co-evolve both the controllers and the environment. A comparison of both methods shows that the co-evolutive approach improves the generality of controllers.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley-Blackwell
dc.rights © Wiley Periodicals
dc.title Generalization capabilities of co-evolution in learning robot behavior
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1002/rob.10054
dc.subject.eciencia Informática
dc.identifier.doi 10.1002/rob.10054
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 455
dc.identifier.publicationissue 10
dc.identifier.publicationlastpage 467
dc.identifier.publicationtitle Journal of Robotic Systems
dc.identifier.publicationvolume 19
dc.identifier.uxxi AR/0000004509
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