research-article

Hyperplane initialized local search for MAXSAT

Authors:

Darrell Whitley,

Wenxiang ChenAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 805 - 812

https://doi.org/10.1145/2463372.2463468

Published: 06 July 2013 Publication History

Abstract

By converting the MAXSAT problem to Walsh polynomials, we can efficiently and exactly compute the hyperplane averages of fixed order k. We use this fact to construct initial solutions based on variable configurations that maximize the sampling of hyperplanes with good average evaluations. The Walsh coefficients can also be used to implement a constant time neighborhood update which is integral to a fast next descent local search for MAXSAT (and for all bounded pseudo-Boolean optimization problems.) We evaluate the effect of initializing local search with hyperplane averages on both the first local optima found by the search and the final solutions found after a fixed number of bit flips. Hyperplane initialization not only provides better evaluations, but also finds local optima closer to the globally optimal solution in fewer bit flips than search initialized with random solutions. A next descent search initialized with hyperplane averages is able to outperform several state-of-the art stochastic local search algorithms on both random and industrial instances of MAXSAT.

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Cited By

Whitley DOchoa GFloyd NChicano F(2024)Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under RecombinationEvolutionary Computation in Combinatorial Optimization10.1007/978-3-031-57712-3_8(113-128)Online publication date: 2024
https://doi.org/10.1007/978-3-031-57712-3_8
Whitley DOchoa GChicano F(2023)Partition Crossover can Linearize Local Optima Lattices of k-bounded Pseudo-Boolean FunctionsProceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3594805.3607129(152-162)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1145/3594805.3607129
Berend DGolan STwitto Y(2022)Using the method of conditional expectations to supply an improved starting point for CCLSJournal of Combinatorial Optimization10.1007/s10878-022-00907-544:5(3711-3734)Online publication date: 30-Sep-2022
https://doi.org/10.1007/s10878-022-00907-5
Show More Cited By

Index Terms

Hyperplane initialized local search for MAXSAT
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Whitley DOchoa GFloyd NChicano F(2024)Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under RecombinationEvolutionary Computation in Combinatorial Optimization10.1007/978-3-031-57712-3_8(113-128)Online publication date: 2024
https://doi.org/10.1007/978-3-031-57712-3_8
Whitley DOchoa GChicano F(2023)Partition Crossover can Linearize Local Optima Lattices of k-bounded Pseudo-Boolean FunctionsProceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3594805.3607129(152-162)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1145/3594805.3607129
Berend DGolan STwitto Y(2022)Using the method of conditional expectations to supply an improved starting point for CCLSJournal of Combinatorial Optimization10.1007/s10878-022-00907-544:5(3711-3734)Online publication date: 30-Sep-2022
https://doi.org/10.1007/s10878-022-00907-5
Chu YLuo CCai SYou H(2019)Empirical investigation of stochastic local search for maximum satisfiabilityFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7107-z13:1(86-98)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s11704-018-7107-z
Neumann FDoerr BLehre PHaddow P(2014)Editorial for the Special Issue on Theoretical Foundations of Evolutionary ComputationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.235067318:5(625-627)Online publication date: Oct-2014
https://doi.org/10.1109/TEVC.2014.2350673
Tseng LChen C(2014)Solving the maximum satisfiability problem by fuzzy converting it into a continuous optimization problem2014 International Conference on Machine Learning and Cybernetics10.1109/ICMLC.2014.7009141(352-358)Online publication date: Jul-2014
https://doi.org/10.1109/ICMLC.2014.7009141
Whitley DChicano F(2014)Elementary Landscape Decomposition of the Hamiltonian Path Optimization Problem,Evolutionary Computation in Combinatorial Optimisation10.1007/978-3-662-44320-0_11(121-132)Online publication date: 2014
https://doi.org/10.1007/978-3-662-44320-0_11

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