On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations

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dc.contributor.authorCorus, Dogan
dc.contributor.authorOliveto, Pietro S.
dc.date.accessioned2026-04-04T18:55:54Z
dc.date.available2026-04-04T18:55:54Z
dc.date.issued2025
dc.departmentİstanbul Bilgi Üniversitesi
dc.description2025 Genetic and Evolutionary Computation Conference Companion-GECCO -- JUL 14-18, 2025 -- Malaga, SPAIN
dc.description.abstractIn this paper we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic Programming (GSGP) system. More specifically, we consider a hill-climber using the GSGP Fixed Block Mutation (FBM) operator for the domain of Boolean functions. We prove that the algorithm cannot evolve Boolean conjunctions of arbitrary size that are correct on unseen inputs chosen uniformly at random from the complete truth table i.e., it generalises poorly. Two algorithms based on the Varying Block Mutation (VBM) operator are proposed and analysed to address the issue. We rigorously prove that under the uniform distribution the first one can efficiently evolve any Boolean function of constant size with respect to the number of available variables, while the second one can efficiently evolve general conjunctions or disjunctions of any size without requiring prior knowledge of the target function class. An experimental analysis confirms the theoretical insights for realistic problem sizes and indicates the superiority of the proposed operators also for small parity functions not explicitly covered by the theory.
dc.identifier.doi10.1145/3712255.3734249
dc.identifier.doi10.1145/3712255.3734249
dc.identifier.endpage88
dc.identifier.isbn979-8-4007-1464-1
dc.identifier.scopus2-s2.0-105014587264
dc.identifier.scopusqualityN/A
dc.identifier.startpage87
dc.identifier.urihttps://doi.org/10.1145/3712255.3734249
dc.identifier.urihttps://hdl.handle.net/11411/10617
dc.identifier.wosWOS:001564494900044
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 Companion, Gecco 2025 Companion
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectGeometric Semantic Genetic Programming
dc.subjectBoolean Functions
dc.subjectRuntime Analysis
dc.titleOn the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations
dc.typeConference Object

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