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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Chicano, Francisco
Ochoa, Gabriela
Tomassini, Marco
Title: Real-like MAX-SAT instances and the landscape structure across the phase transition
Editor(s): Chicano, Francisco
Citation: Chicano F, Ochoa G & Tomassini M (2021) Real-like MAX-SAT instances and the landscape structure across the phase transition. In: Chicano F (ed.) GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Lille, France, 10.07.2021-14.07.2021. New York: Association for Computing Machinery, Inc, pp. 207-215.
Issue Date: Jun-2021
Date Deposited: 4-Aug-2021
Conference Name: 2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Conference Dates: 2021-07-10 - 2021-07-14
Conference Location: Lille, France
Abstract: In contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms. It is believed that this is the consequence of the non-random structure of the distribution of variables into clauses. In order to produce benchmark instances resembling those of real-world formulas with a given structure, generative models have been proposed. In this paper we study the MAX-3SAT problem with model-generated instances having a power-law distribution. Specifically, we target the regions in which computational difficulty undergoes an easy/hard phase transition as a function of clause density and of the power-law exponent. Our approach makes use of a sampling technique to build a graph model (a local optima network) in which nodes are local optima and directed edges are transitions between optima basins. The objective is to relate the structure of the instance fitness landscape with problem difficulty through the transition. We succeed in associating the transition with straightforward network metrics, thus providing a novel and original fitness landscape view of the computational features of the power-law model and its phase transition.
Status: AM - Accepted Manuscript
Rights: © ACM, 2021. 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 ’21, July 10–14, 2021, Lille, France 2021. ACM ISBN 978-1-4503-8350-9/21/07.

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