On binary unbiased operators returning multiple offspring

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

1 Dyfyniad (Scopus)

Crynodeb

The notion of unbiased black-box complexity plays an important role in theory of randomized search heuristics. A black-box algorithm is usually defined as an algorithm which uses unbiased variation operations. In all known papers, the analysed variation operators take k arguments and produce one offspring. On the other hand, many practitioners use crossovers which produce two offspring, and in many living organisms a diploid cell produces two distinct haploid genotypes. We investigate how the binary-To-binary, or (2 → 2), unbiased variation operators look like, and how they can be used to improve randomized search heuristics. We show that the (2 → 2) unbiased black-box complexity of Needle coincides with its unrestricted black-box complexity. We also show that it can be used to put strong worst-case guarantees for solving OneMax.

Iaith wreiddiolSaesneg
TeitlGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
CyhoeddwrAssociation for Computing Machinery, Inc
Tudalennau1395-1398
Nifer y tudalennau4
ISBN (Electronig)9781450349390
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 15 Gorff 2017
Cyhoeddwyd yn allanolIe
Digwyddiad2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Yr Almaen
Hyd: 15 Gorff 201719 Gorff 2017

Cyfres gyhoeddiadau

EnwGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion

Cynhadledd

Cynhadledd2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017
Gwlad/TiriogaethYr Almaen
DinasBerlin
Cyfnod15 Gorff 201719 Gorff 2017

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