|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Comparing Communities of Optima with Funnels in Combinatorial Fitness Landscapes|
|Citation:||Thomson 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), New York: ACM. GECCO ’17: The Genetic and Evolutionary Computation Conference, 15.7.2017 - 19.7.2017, Berlin, Germany.|
|Conference Name:||GECCO ’17: The Genetic and Evolutionary Computation Conference|
|Conference Location:||Berlin, Germany|
|Abstract:||The 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.|
|Status:||Book Chapter: author post-print (pre-copy editing)|
|Rights:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. © 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)|
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