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Reactive Search Optimization: Learning While Optimizing

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Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 146))

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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).

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Notes

  1. 1.

    http://reactive-search.org/

  2. 2.

    http://reactive-search.com/

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  1. LION Lab, Università di Trento, Trento, Italy

    Roberto Battiti & Mauro Brunato

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  1. Roberto Battiti
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  2. Mauro Brunato
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Correspondence to Roberto Battiti or Mauro Brunato .

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  1. , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, H3C 3A7, Québec, Canada

    Michel Gendreau

  2. Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Pavillon André-Aisenstadt, Montreal, H3C 3J7, Québec, Canada

    Jean-Yves Potvin

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Battiti, R., Brunato, M. (2010). Reactive Search Optimization: Learning While Optimizing. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1665-5_18

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