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A survey on dynamic populations in bio-inspired algorithms

2024-12Recensão Acesso aberto
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dc.contributor.authorFarinati, Davide
dc.contributor.authorVanneschi, Leonardo
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblSpringer Science Business Media
dc.date.accessioned2024-07-29T22:14:38Z
dc.date.available2024-07-29T22:14:38Z
dc.date.issued2024-12
dc.descriptionFarinati, D., & Vanneschi, L. (2024). A survey on dynamic populations in bio-inspired algorithms. Genetic Programming And Evolvable Machines, 25(2), 1-32. Article 19. https://doi.org/10.1007/s10710-024-09492-4 --- Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS (https://doi.org/10.54499/UIDB/04152/2020).
dc.description.abstractPopulation-Based Bio-Inspired Algorithms (PBBIAs) are computational methods that simulate natural biological processes, such as evolution or social behaviors, to solve optimization problems. Traditionally, PBBIAs use a population of static size, set beforehand through a specific parameter. Nevertheless, for several decades now, the idea of employing populations of dynamic size, capable of adjusting during the course of a single run, has gained ground. Various methods have been introduced, ranging from simpler ones that use a predefined function to determine the population size variation, to more sophisticated methods where the population size in different phases of the evolutionary process depends on the dynamics of the evolution itself and events occurring within the population during the run. The common underlying idea in many of these approaches, is similar: to save a significant amount of computational effort in phases where the evolution is functioning well, and therefore a large population is not needed. This allows for reusing the previously saved computational effort when optimization becomes more challenging, and hence a greater computational effort is required. Numerous past contributions have demonstrated a notable advantage of using dynamically sized populations, often resulting in comparable results to those obtained by the standard PBBIAs but with a significant saving of computational effort. However, despite the numerous successes that have been presented, to date, there is still no comprehensive collection of past contributions on the use of dynamic populations that allows for their categorization and critical analysis. This article aims to bridge this gap by presenting a systematic literature review regarding the use of dynamic populations in PBBIAs, as well as identifying gaps in the research that can lead the path to future works.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent32
dc.format.extent792732
dc.identifier.doi10.1007/s10710-024-09492-4
dc.identifier.issn1389-2576
dc.identifier.otherPURE: 96124299
dc.identifier.otherPURE UUID: 9f1e4073-89f6-41db-9a71-32cd4ff61f81
dc.identifier.othercrossref: 10.1007/s10710-024-09492-4
dc.identifier.otherScopus: 85199380824
dc.identifier.otherWOS: 001275562900001
dc.identifier.otherORCID: /0000-0003-4732-3328/work/164536078
dc.identifier.urihttp://hdl.handle.net/10362/170138
dc.identifier.urlhttps://www.scopus.com/pages/publications/85199380824
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001275562900001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectPopulation-based algorithms
dc.subjectBio-inspired algorithms
dc.subjectPopulation size
dc.subjectDynamic population
dc.subjectAdaptive population
dc.subjectSoftware
dc.subjectTheoretical Computer Science
dc.subjectHardware and Architecture
dc.subjectComputer Science Applications
dc.titleA survey on dynamic populations in bio-inspired algorithmsen
dc.typereview
degois.publication.firstPage1
degois.publication.issue2
degois.publication.lastPage32
degois.publication.titleGenetic Programming And Evolvable Machines
degois.publication.volume25
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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