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Reactive Search Optimization: Learning While Optimizing

In: Handbook of Metaheuristics

Author

Listed:
  • Roberto Battiti

    (LION Lab, Università di Trento)

  • Mauro Brunato

    (LION Lab, Università di Trento)

Abstract

Reactive Search Optimization advocates the integration of sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. The word reactive hints at a ready response to events during the search through an internal online feedback loop for the self-tuning of critical parameters. Methodologies of interest for Reactive Search Optimization include machine learning and statistics, in particular reinforcement learning, active or query learning, neural networks, and meta-heuristics (although the boundary signalled by the “meta” prefix is not always clear).

Suggested Citation

  • Roberto Battiti & Mauro Brunato, 2010. "Reactive Search Optimization: Learning While Optimizing," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 543-571, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_18
    DOI: 10.1007/978-1-4419-1665-5_18
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    Cited by:

    1. Sergio Valdivia & Ricardo Soto & Broderick Crawford & Nicolás Caselli & Fernando Paredes & Carlos Castro & Rodrigo Olivares, 2020. "Clustering-Based Binarization Methods Applied to the Crow Search Algorithm for 0/1 Combinatorial Problems," Mathematics, MDPI, vol. 8(7), pages 1-42, July.

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