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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Thomson, Sarah
Ochoa, Gabriela
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Title: On Funnel Depths and Acceptance Criteria in Stochastic Local Search
Citation: Thomson S & Ochoa G (2022) On Funnel Depths and Acceptance Criteria in Stochastic Local Search. In: GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference (GECCO) 2022, Boston, USA, 09.07.2022-13.07.2022. New York: ACM, pp. 287-295.;
Issue Date: 2022
Date Deposited: 7-Jun-2022
Conference Name: Genetic and Evolutionary Computation Conference (GECCO) 2022
Conference Dates: 2022-07-09 - 2022-07-13
Conference Location: Boston, USA
Abstract: We propose looking at the phenomenon of fitness landscape funnels in terms of their depth. In particular, we examine how the depth of funnels in Local Optima Networks (LONs) of benchmark Quadratic Assignment Problem instances relate to metaheuristic performance. Three distinct iterated local search (ILS) acceptance strategies are considered: better-or-equal (standard), annealing-like, and restart. Funnel measurements are analysed for their connection to ILS performance on the underlying combinatorial problems. We communicate the findings through hierarchical clustering of LONs, network visualisations, subgroup analysis, correlation analysis, and Random Forest regression models. The results show that funnel depth is associated with search difficulty, and that there is an interplay between funnel structure and acceptance strategy. Standard and annealing acceptance work better than restart on both deep-funnel and shallow-funnel problems; standard acceptance is the best strategy when optimal funnel(s) are deep, while annealing acceptance is superior when they are shallow. Regression models including funnel depth measurements could explain up to 96% of ILS runtime variance (with annealing-like acceptance). The runtime of ILS with restarts was less explainable using funnel features.
Status: AM - Accepted Manuscript
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. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO ’22, July 9–13, 2022, Boston, MA, USA
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