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Choosing Search Heuristics by Non-Stationary Reinforcement Learning

  • Alexander Nareyek
Chapter
Part of the Applied Optimization book series (APOP, volume 86)

Abstract

Search decisions are often made using heuristic methods because real-world applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learns, during the search process, how to select promising heuristics. The learning is based on weight adaptation and can even switch between different heuristics during search. Different variants of the approach are evaluated within a constraint-programming environment.

Keywords

Non-stationary reinforcement learning Optimization Local search Constraint programming. 

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Alexander Nareyek
    • 1
  1. 1.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA

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