FP-AK-QIEA-R for Multi-Objective optimization

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dc.contributor.authorSaire, Josimar
dc.date.accessioned2019-01-29T22:19:52Z
dc.date.available2019-01-29T22:19:52Z
dc.date.issued2016
dc.description.abstractThe Evolutionary Algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial population to sample new population and uses rewarding criteria to sample around the best of every iteration using cumulative density function estimated for Akima interpolation, it was used for mono-objective problems showing good results. The proposal uses the algorithm FP-AKQIEA-R and add Pareto dominance to experiment with multiobjective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm. © 2016 ACM.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.identifier.doihttps://doi.org/10.1145/3022702.3022714es_PE
dc.identifier.isbnurn:isbn:9781450348249es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15827
dc.language.isoenges_PE
dc.publisherAssociation for Computing Machineryes_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85014862631&doi=10.1145%2f3022702.3022714&partnerID=40&md5=46c1af0f44069678768297d9e398f2bdes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectBioinformaticses_PE
dc.subjectInterpolationes_PE
dc.subjectIterative methodses_PE
dc.subjectMultiobjective optimizationes_PE
dc.subjectOptimizationes_PE
dc.subjectProbability density functiones_PE
dc.subjectBenchmark functionses_PE
dc.subjectCumulative density functionses_PE
dc.subjectEvolutionary operationses_PE
dc.subjectInitial populationes_PE
dc.subjectMulti-objective problemes_PE
dc.subjectParticle filteres_PE
dc.subjectPDF estimationes_PE
dc.subjectQuantum inspired evolutionary algorithmes_PE
dc.subjectEvolutionary algorithmses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleFP-AK-QIEA-R for Multi-Objective optimizationes_PE
dc.typeinfo:eu-repo/semantics/conferenceObject
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