dc.creator | Zhang, Gexiang | es |
dc.creator | Rong, Haina | es |
dc.creator | Neri, Ferrante | es |
dc.creator | Pérez Jiménez, Mario de Jesús | es |
dc.date.accessioned | 2021-04-27T11:39:04Z | |
dc.date.available | 2021-04-27T11:39:04Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Zhang, G., Rong, H., Neri, F. y Pérez Jiménez, M.d.J. (2014). An optimization Spiking Neural P system for approximately solving combinatorial optimization problems. International Journal of Neural Systems (IJNS), 24 (5), 1440006-1-1440006-16. | |
dc.identifier.issn | 0129-0657 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107946 | |
dc.description.abstract | Membrane systems (also called P systems) refer to the computing models abstracted from the structure and
the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other
populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing
models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization
problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are
named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design
a P system for directly obtaining the approximate solutions of combinatorial optimization problems
without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural
P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and
multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are
further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve
combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to
experimentally prove the viability and effectiveness of the proposed neural system. | es |
dc.format | application/pdf | es |
dc.format.extent | 16 | es |
dc.language.iso | eng | es |
dc.publisher | World Scientific | es |
dc.relation.ispartof | International Journal of Neural Systems (IJNS), 24 (5), 1440006-1-1440006-16. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Membrane Computing | es |
dc.subject | Spiking neural P Systems | es |
dc.subject | Extended spiking neural P system | es |
dc.subject | Optimization spiking neural P system | es |
dc.subject | Knapsack problem | es |
dc.title | An optimization Spiking Neural P system for approximately solving combinatorial optimization problems | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.publisherversion | https://www.worldscientific.com/doi/abs/10.1142/S0129065714400061 | es |
dc.identifier.doi | 10.1142/S0129065714400061 | es |
dc.contributor.group | Universidad de Sevilla. TIC193: Computación Natural | es |
dc.journaltitle | International Journal of Neural Systems (IJNS) | es |
dc.publication.volumen | 24 | es |
dc.publication.issue | 5 | es |
dc.publication.initialPage | 1440006-1 | es |
dc.publication.endPage | 1440006-16 | es |
dc.identifier.sisius | 20739216 | es |