Effect of Initial Assignment on Local Search Performance for Max Sat

Daniel Berend, Yochai Twitto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


In this paper, we explore the correlation between the quality of initial assignments provided to local search heuristics and that of the corresponding final assignments. We restrict our attention to the Max r-Sat problem and to one of the leading local search heuristics-Configuration Checking Local Search (CCLS). We use a tailored version of the Method of Conditional Expectations (MOCE) to generate initial assignments of diverse quality. We show that the correlation in question is significant and long-lasting. Namely, even when we delve deeper into the local search, we are still in the shadow of the initial assignment. Thus, under practical time constraints, the quality of the initial assignment is crucial to the performance of local search heuristics. To demonstrate our point, we improve CCLS by combining it with MOCE. Instead of starting CCLS from random initial assignments, we start it from excellent initial assignments, provided by MOCE. Indeed, it turns out that this kind of initialization provides a significant improvement of this state-of-the-art solver. This improvement becomes more and more significant as the instance grows. 2012 ACM Subject Classification Theory of computation ! Theory of randomized search heuristics; Theory of computation ! Stochastic approximation.

Original languageEnglish
Title of host publication18th International Symposium on Experimental Algorithms, SEA 2020
EditorsSimone Faro, Domenico Cantone
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959771481
StatePublished - 1 Jun 2020
Event18th International Symposium on Experimental Algorithms, SEA 2020 - Catania, Italy
Duration: 16 Jun 202018 Jun 2020

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
ISSN (Print)1868-8969


Conference18th International Symposium on Experimental Algorithms, SEA 2020


  • Combinatorial optimization
  • Local search
  • Maximum satisfiability
  • Probabilistic algorithms

ASJC Scopus subject areas

  • Software


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