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Guiding Stochastic Search by Dynamic Learning of the Problem Topography

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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5015))

Abstract

We study experimentally the effect of dynamic learning of the problem topographic characteristics on stochastic search.

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References

  1. Naveh, Y.: Stochastic solver for constraint satisfaction problems with learning of high-level characteristics of the problem topography. In: Proceedings of the 1st International Workshop on Local Search Techniques in Constraint Satisfaction (LSCS 2004). (2004)

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  2. Naveh, Y., Rimon, M., Jaeger, I., Katz, Y., Vinov, M., Marcus, E., Shurek, G.: Constraint-based random stimuli generation for hardware verification. AI Magazine 28, 13–30 (2007)

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  3. Sabato, S., Naveh, Y.: Preprocessing expression-based constraint satisfaction problems for stochastic local search. In: Van Hentenryck, P., Wolsey, L.A. (eds.) CPAIOR 2007. LNCS, vol. 4510, pp. 244–259. Springer, Heidelberg (2007)

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  4. Burke, E.K., Kendall, G.N., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-Heuristics: An Emerging Direction in Modern Search Technology. In: Handbook of Meta-Heuristics, pp. 457–474. Springer, Heidelberg (2003)

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Author information

Authors and Affiliations

  1. IBM Haifa Research Lab, Haifa University Campus, Haifa, 31905, Israel

    Yehuda Naveh

Authors
  1. Yehuda Naveh
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Editor information

Laurent Perron Michael A. Trick

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© 2008 Springer-Verlag Berlin Heidelberg

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Naveh, Y. (2008). Guiding Stochastic Search by Dynamic Learning of the Problem Topography. In: Perron, L., Trick, M.A. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2008. Lecture Notes in Computer Science, vol 5015. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68155-7_36

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  • DOI: https://doi.org/10.1007/978-3-540-68155-7_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68154-0

  • Online ISBN: 978-3-540-68155-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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