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dc.contributor.authorDel Ser, Javier
dc.contributor.authorOsaba, Eneko
dc.contributor.authorMolina Cabrera, Daniel 
dc.contributor.authorYang, Xin-She
dc.contributor.authorSalcedo-Sans, Sancho
dc.contributor.authorCamacho, David
dc.contributor.authorSwagatam, Das
dc.contributor.authorPonnuthurai N., Suganthan
dc.contributor.authorCoello Coello, Carlos A.
dc.contributor.authorHerrera Triguero, Francisco 
dc.date.accessioned2024-01-24T12:44:56Z
dc.date.available2024-01-24T12:44:56Z
dc.date.issued2019-08
dc.identifier.citationJ. Del Ser et al., «Bio-inspired computation: Where we stand and what’s next», Swarm and Evolutionary Computation, vol. 48, pp. 220-250, ago. 2019, doi: 10.1016/j.swevo.2019.04.008.es_ES
dc.identifier.urihttps://hdl.handle.net/10481/87213
dc.description.abstractIn recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.es_ES
dc.language.isoenges_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBio-inspired Computationes_ES
dc.subjectEvolutionary Computationes_ES
dc.subjectSwarm Intelligencees_ES
dc.subjectNature-inspired Computationes_ES
dc.subjectDynamic Optimizationes_ES
dc.subjectMulti-objective Optimizationes_ES
dc.subjectMany-objective Optimizationes_ES
dc.subjectMulti-modal Optimizationes_ES
dc.subjectLarge-Scale Global Optimizationes_ES
dc.subjectTopologies es_ES
dc.subjectEnsembleses_ES
dc.subjectHyper-heuristicses_ES
dc.subjectSurrogate model assisted optimizationes_ES
dc.subjectComputationally Expensive Optimizationes_ES
dc.subjectDistributed Evolutionary Computationes_ES
dc.subjectMemetic Algorithmses_ES
dc.subjectParameter Tuninges_ES
dc.subjectParameter Adaptationes_ES
dc.subjectBenchmarkses_ES
dc.titleBio-inspired Computation: Where We Stand and What’s Nextes_ES
dc.typejournal articlees_ES
dc.rights.accessRightsembargoed accesses_ES
dc.identifier.doi10.1016/j.swevo.2019.04.008
dc.type.hasVersionAMes_ES


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