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A Hybrid Search Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences

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Book cover Principles and Practice of Constraint Programming – CP 2000 (CP 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1894))

Abstract

The hybridisation of systematic and stochastic search is an active research area with potential benefits for real-world combinatorial problems. This paper shows that randomising the backtracking component of a systematic backtracker can improve its scalability to equal that of stochastic local search. The hybrid may be viewed as stochastic local search in a constrained space, cleanly combining local search with constraint programming techniques. The approach is applied to two very different problems. Firstly a hybrid of local search and constraint propagation is applied to hard random 3-SAT problems, and is the first constructive search algorithm to solve very large instances. Secondly a hybrid of local search and branch-and-bound is applied to low-autocorrelation binary sequences (a notoriously difficult communications engineering problem), and is the first stochastic search algorithm to find optimal solutions. These results show that the approach is a promising one for both constraint satisfaction and optimisation problems.

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Authors and Affiliations

  1. Department of Computer Science, National University of Ireland at Cork, Ireland

    Steven Prestwich

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  1. Steven Prestwich
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Editors and Affiliations

  1. Department of Computer Science, University of California at Irvine, Irvine, CA, 92697, USA

    Rina Dechter

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Prestwich, S. (2000). A Hybrid Search Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences. In: Dechter, R. (eds) Principles and Practice of Constraint Programming – CP 2000. CP 2000. Lecture Notes in Computer Science, vol 1894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45349-0_25

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  • DOI: https://doi.org/10.1007/3-540-45349-0_25

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  • Print ISBN: 978-3-540-41053-9

  • Online ISBN: 978-3-540-45349-9

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