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  • Diversify Intensification Phases in Local Search for SAT with a New Probability Distribution

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    Author(s)
    Duong, TT
    Pham, DN
    Sattar, A
    Griffith University Author(s)
    Sattar, Abdul
    Pham, Nghia N.
    Duong, Thach-Thao Nguyen N.
    Year published
    2013
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    Abstract
    A key challenge in developing efficient local search solvers is to intelligently balance diversification and intensification. This study pro- poses a heuristic that integrates a new dynamic scoring function and two different diversification criteria: variable weights and stagnation weights. Our new dynamic scoring function is formulated to enhance the diversifi- cation capability in intensification phases using a user-defined diversifica- tion parameter. The formulation of the new scoring function is based on a probability distribution to adjust the selecting priorities of the selection between greediness on scores ...
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    A key challenge in developing efficient local search solvers is to intelligently balance diversification and intensification. This study pro- poses a heuristic that integrates a new dynamic scoring function and two different diversification criteria: variable weights and stagnation weights. Our new dynamic scoring function is formulated to enhance the diversifi- cation capability in intensification phases using a user-defined diversifica- tion parameter. The formulation of the new scoring function is based on a probability distribution to adjust the selecting priorities of the selection between greediness on scores and diversification on variable properties. The probability distribution of variables on greediness is constructed to guarantee the synchronization between the probability distribution func- tions and score values. Additionally, the new dynamic scoring function is integrated with the two diversification criteria. The experiments show that the new heuristic is efficient on verification benchmark, crafted and random instances.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    8272 LNAI
    Publisher URI
    http://ai2013.otago.ac.nz
    DOI
    https://doi.org/10.1007/978-3-319-03680-9_18
    Copyright Statement
    © 2013 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/55739
    Collection
    • Conference outputs

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