Article

Evidence for invariants in local search

Authors:

David McAllester,

Henry KautzAuthors Info & Claims

AAAI'97/IAAI'97: Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

Pages 321 - 326

Published: 27 July 1997 Publication History

Abstract

It is well known that the performance of a stochastic local search procedure depends upon the setting of its noise parameter, and that the optimal setting varies with the problem distribution. It is therefore desirable to develop general priniciples for tuning the procedures. We present two statistical measures of the local search process that allow one to quickly find the optimal noise settings. These properties are independent of the fine details of the local search strategies, and appear to be relatively independent of the structure of the problem domains. We applied these principles to the problem of evaluating new search heuristics, and discovered two promising new strategies.

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Digital Library

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Kautz, H., and Selman, B. (1996). Pushing the envelope: planning, propositional logic, and stochastic search. Proc. AAAI-96, Portland, OR, 1996.

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Cited By

Bouhmala N(2019)Combining simulated annealing with local search heuristic for MAX-SATJournal of Heuristics10.1007/s10732-018-9386-925:1(47-69)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10732-018-9386-9
Liu SMelo GSingh SMarkovitch S(2017)Should algorithms for random SAT and Max-SAT be different?Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298023.3298137(3915-3921)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298023.3298137
Sassa SKanazawa KCai SYasunaga M(2017)An FPGA Solver for Partial MaxSAT Problems Based on Stochastic Local SearchACM SIGARCH Computer Architecture News10.1145/3039902.303990944:4(32-37)Online publication date: 11-Jan-2017
https://dl.acm.org/doi/10.1145/3039902.3039909
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Evidence for invariants in local search
1. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis

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cover image Guide Proceedings

AAAI'97/IAAI'97: Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

July 1997

1085 pages

ISBN:0262510952

Publisher

AAAI Press

Publication History

Published: 27 July 1997

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Cited By

Bouhmala N(2019)Combining simulated annealing with local search heuristic for MAX-SATJournal of Heuristics10.1007/s10732-018-9386-925:1(47-69)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10732-018-9386-9
Liu SMelo GSingh SMarkovitch S(2017)Should algorithms for random SAT and Max-SAT be different?Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298023.3298137(3915-3921)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298023.3298137
Sassa SKanazawa KCai SYasunaga M(2017)An FPGA Solver for Partial MaxSAT Problems Based on Stochastic Local SearchACM SIGARCH Computer Architecture News10.1145/3039902.303990944:4(32-37)Online publication date: 11-Jan-2017
https://dl.acm.org/doi/10.1145/3039902.3039909
Xian AZhu KZhu DPu LLiu H(2017)Approximating Max NAE-k-SAT by anonymous local searchTheoretical Computer Science10.1016/j.tcs.2016.05.040657:PA(54-63)Online publication date: 2-Jan-2017
https://dl.acm.org/doi/10.1016/j.tcs.2016.05.040
Abramé AHabet DToumi D(2017)Improving configuration checking for satisfiable random k-SAT instancesAnnals of Mathematics and Artificial Intelligence10.1007/s10472-016-9515-979:1-3(5-24)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10472-016-9515-9
Li RHu SWang YYin M(2017)A local search algorithm with tabu strategy and perturbation mechanism for generalized vertex cover problemNeural Computing and Applications10.1007/s00521-015-2172-928:7(1775-1785)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s00521-015-2172-9
Bouhmala N(2016)A variable neighbourhood search structure based-genetic algorithm for combinatorial optimisation problemsInternational Journal of Intelligent Systems Technologies and Applications10.1504/IJISTA.2016.07649415:2(127-146)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1504/IJISTA.2016.076494
Harris SBueter TTauritz DEsparcia-Alcázar A(2015)A Comparison of Genetic Programming Variants for Hyper-HeuristicsProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768456(1043-1050)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2768456
Chockler HIvrii AMatsliah ARollini SSharygina NShin SMaldonado J(2013)Using cross-entropy for satisfiabilityProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480588(1196-1203)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2480362.2480588
Arbelaez ACodognet P(2013)From sequential to parallel local search for SATProceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization10.1007/978-3-642-37198-1_14(157-168)Online publication date: 3-Apr-2013
https://dl.acm.org/doi/10.1007/978-3-642-37198-1_14
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