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Learning in Search

In: Hybrid Optimization

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  • Philippe Refalo

    (IBM, Les Taissounieres)

Abstract

This chapter focuses on the recent improvements in solution search that are based on learning. We will describe some learning methods applied in areas such as mixed-integer programming, constraint programming, and those used for satisfaction problems. Instead of being exhaustive, we will concentrate on some of the most exciting advances. In particular, we will focus on pseudo-cost strategies used in general-purpose mixed-integer programming solvers, on the strategy learning used for automatic search in constraint programming, and on no-good generation in SAT solvers. Several examples are given to illustrate the effectiveness of learning in these areas. Some practical results are also given using the integration of different learning techniques.

Suggested Citation

  • Philippe Refalo, 2011. "Learning in Search," Springer Optimization and Its Applications, in: Pascal van Hentenryck & Michela Milano (ed.), Hybrid Optimization, edition 1, pages 337-356, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-1644-0_10
    DOI: 10.1007/978-1-4419-1644-0_10
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