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dc.contributor.authorArza E.en_US
dc.contributor.authorCeberio J.en_US
dc.contributor.authorPérez A.en_US
dc.contributor.authorIrurozki E.en_US
dc.date.accessioned2020-07-26T14:48:59Z
dc.date.available2020-07-26T14:48:59Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1137
dc.description.abstractAccording to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic approaches, at the cost of a more complex design and parameter tuning process. Trying to combine the best of both worlds, the field of hyperheuristics investigates the automatized generation and hybridization of heuristic algorithms. In this paper, we propose a neuroevolution-based hyperheuristic approach. Particularly, we develop a population-based hyperheuristic algorithm that first trains a neural network on an instance of a problem and then uses the trained neural network to control how and which low-level operators are applied to each of the solutions when optimizing different problem instances. The trained neural network maps the state of the optimization process to the operations to be applied to the solutions in the population at each generation.en_US
dc.description.sponsorshipTIN2016-78365R BERC 2014-2017 Research Groups 2013-2018 (IT-609-13).en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.publisherThe Genetic and Evolutionary Computation Conferenceen_US
dc.relationES/1PE/SEV-2017-0718en_US
dc.relationES/1PE/TIN2017-82626-Ren_US
dc.relationEUS/ELKARTEKen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectneuroevolutionen_US
dc.subjecttransfer learningen_US
dc.subjecthyperheuristicen_US
dc.subjectoptimizationen_US
dc.titleAn adaptive neuroevolution-based hyperheuristicen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeinfo:eu-repo/semantics/publishedVersionen_US
dc.identifier.doi10.1145/3377929.3389937
dc.relation.publisherversionhttps://dl.acm.org/doi/abs/10.1145/3377929.3389937en_US


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