Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorThomson, Sarahen_UK
dc.contributor.authorDaolio, Fabioen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.description.abstractThe existence of sub-optimal funnels in combinatorial fitness landscapes has been linked to search difficulty. The exact nature of these structures — and how commonly they appear — is not yet fully understood. Improving our understanding of funnels could help with designing effective diversification mechanisms for a ‘smoothing’ effect, making optimisation easier. We model fitness landscapes as local optima networks. The relationship between communities of local optima found by network clustering algorithms and funnels is explored. Funnels are identified using the notion of monotonic sequences from the study of energy landscapes in theoretical chemistry. NK Landscapes and the Quadratic Assignment Problem are used as case studies. Our results show that communities are linked to funnels. The analysis exhibits relationships between these landscape structures and the performance of trajectory-based metaheuristics such as Simulated Annealing (SA) and Iterated Local Search (ILS). In particular, ILS gets trapped in funnels, and modular communities of optima slow it down. The funnels contribute to lower success for SA. We show that increasing the strength of ILS perturbation helps to ‘smooth’ the funnels and improves performance in multi-funnel landscapes.en_UK
dc.relationThomson S, Daolio F & Ochoa G (2017) Comparing Communities of Optima with Funnels in Combinatorial Fitness Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany, July 15–19, 2017 (GECCO ’17). GECCO ’17: The Genetic and Evolutionary Computation Conference, Berlin, Germany, 15.07.2017-19.07.2017. New York: ACM, pp. 377-384.
dc.rights© 2017 Copyright held by the owner/author(s). GECCO ’17, Berlin, Germany Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro fit or commercial advantage and that copies bear this notice and the full citation on the fi rst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)en_UK
dc.subjectFitness Landscapesen_UK
dc.subjectNK Landscapesen_UK
dc.subjectQuadratic Assignment Problemen_UK
dc.subjectLocal Optima Networksen_UK
dc.subjectFunnel Landscapesen_UK
dc.subjectCombinatorial Optimisationen_UK
dc.titleComparing Communities of Optima with Funnels in Combinatorial Fitness Landscapesen_UK
dc.typeConference Paperen_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.citation.btitleProceedings of the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany, July 15–19, 2017 (GECCO ’17)en_UK
dc.citation.conferencedates2017-07-15 - 2017-07-19en_UK
dc.citation.conferencelocationBerlin, Germanyen_UK
dc.citation.conferencenameGECCO ’17: The Genetic and Evolutionary Computation Conferenceen_UK
dc.publisher.addressNew Yorken_UK
dc.description.notesAuthors listed as ECOM Tracken_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.relation.funderprojectDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
local.rioxx.authorThomson, Sarah|0000-0001-6971-7817en_UK
local.rioxx.authorDaolio, Fabio|0000-0003-4240-4161en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.projectEP/J017515/1|Engineering and Physical Sciences Research Council|
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

Files in This Item:
File Description SizeFormat 
thomson.pdfFulltext - Accepted Version1.03 MBAdobe PDFView/Open

This item is protected by original copyright

A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.