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https://hdl.handle.net/2440/139338
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Type: | Conference paper |
Title: | Evolutionary Diversity Optimisation in Constructing Satisfying Assignments |
Author: | Nikfarjam, A. Rothenberger, R. Neumann, F. Friedrich, T. |
Citation: | Proceedings of the Genetic and Evolutionary Computation Conference (GECCO, 2023), 2023 / Paquete, L. (ed./s), vol.abs/2305.11457, pp.938-945 |
Publisher: | Association for Computing Machinery |
Publisher Place: | Online |
Issue Date: | 2023 |
ISBN: | 9798400701191 |
Conference Name: | Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2023 - 19 Jul 2023 : Lisbon, Portugal) |
Editor: | Paquete, L. |
Statement of Responsibility: | Adel Nikfarjam, Ralf Rothenberger, Frank Neumann, Tobias Friedrich |
Abstract: | Computing diverse solutions for a given problem, in particular evolutionary diversity optimisation (EDO), is a hot research topic in the evolutionary computation community. This paper studies the Boolean satis!ability problem (SAT) in the context of EDO. SAT is of great importance in computer science and di"ers from the other problems studied in EDO literature, such as KP and TSP. SAT is heavily constrained, and the conventional evolutionary operators are ine#cient in generating SAT solutions. Our approach avails of the following characteristics of SAT: 1) the possibility of adding more constraints (clauses) to the problem to forbid solutions or to !x variables, and 2) powerful solvers in the literature, such as minisat. We utilise such a solver to construct a diverse set of solutions. Moreover, maximising diversity provides us with invaluable information about the solution space of a given SAT problem, such as how large the feasible region is. In this study, we introduce evolutionary algorithms (EAs) employing a well-known SAT solver to maximise diversity among a set of SAT solutions explicitly. The experimental investigations indicate the introduced algorithms’ capability to maximise diversity among the SAT solutions. |
Keywords: | SAT; Evolutionary Diversity Optimisation |
Rights: | © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. |
DOI: | 10.1145/3583131.3590517 |
Grant ID: | http://purl.org/au-research/grants/arc/DP190103894 http://purl.org/au-research/grants/arc/FT200100536 |
Published version: | http://dx.doi.org/10.1145/3583131.3590517 |
Appears in Collections: | Computer Science publications |
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