Hybrid Selection Allows Steady-State Evolutionary Algorithms to Control the Selective Pressure in Multimodal Optimisation

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dc.authorid0009-0006-2889-0334
dc.contributor.authorCorus, Dogan
dc.contributor.authorOliveto, Pietro S.
dc.contributor.authorZheng, Feiyang
dc.date.accessioned2026-04-04T18:55:55Z
dc.date.available2026-04-04T18:55:55Z
dc.date.issued2025
dc.departmentİstanbul Bilgi Üniversitesi
dc.description2025 Genetic and Evolutionary Computation Conference Companion-GECCO -- JUL 14-18, 2025 -- Malaga, SPAIN
dc.description.abstractRecent work has shown that Inverse Tournament Selection operators within steady-state evolutionary algorithms (EAs) allow to control the selective pressure much more accurately than in generational EAs. However, to achieve low selective pressures, large tournament sizes are required which come at the cost of prohibitive expected times for the population to escape from local optima. To this end, we propose a hybrid selection mechanism that leads to considerable speed-ups in the expected time to escape from local optima while permitting to keep the selective pressure arbitrarily low and the use of large population sizes. The mechanism simply switches between Inverse Elitist selection and Uniform selection when it detects that the population is stuck on local optima, and switches back when an improving solution is found. We prove its effectiveness for the TruncatedTwomax.. and RidgeWithBranches.. benchmarks from the literature by providing super-linear speed-ups over the (.. +1) EA with any fixed selective pressure.
dc.identifier.doi10.1145/3712256.3726411
dc.identifier.doi10.1145/3712256.3726411
dc.identifier.endpage889
dc.identifier.isbn979-8-4007-1465-8
dc.identifier.scopus2-s2.0-105013079275
dc.identifier.scopusqualityN/A
dc.identifier.startpage881
dc.identifier.urihttps://doi.org/10.1145/3712256.3726411
dc.identifier.urihttps://hdl.handle.net/11411/10618
dc.identifier.wosWOS:001556459900100
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAssoc Computing Machinery
dc.relation.ispartofProceedings of the 2025 Genetic and Evolutionary Computation Conference, Gecco 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectPopulations
dc.subjectSelective Pressure
dc.subjectHybridisation
dc.subjectSelf-Adaptation
dc.titleHybrid Selection Allows Steady-State Evolutionary Algorithms to Control the Selective Pressure in Multimodal Optimisation
dc.typeConference Object

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