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Reactive Search Optimization: Learning While Optimizing

In: Handbook of Metaheuristics

Author

Listed:
  • Roberto Battiti

    (University of Trento)

  • Mauro Brunato

    (University of Trento)

  • Andrea Mariello

    (University of Trento)

Abstract

Reactive Search Optimization (RSO) 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 include prohibition-based methods, reactions on the neighborhood, the annealing schedule or the objective function, and reactions in population-based methods. This chapter describes different strategies that have been introduced in the literature as well as several applications to classic combinatorial tasks, continuous optimization and real-world problems.

Suggested Citation

  • Roberto Battiti & Mauro Brunato & Andrea Mariello, 2019. "Reactive Search Optimization: Learning While Optimizing," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 479-511, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-91086-4_15
    DOI: 10.1007/978-3-319-91086-4_15
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    Cited by:

    1. Kaveh, Mehrdad & Mesgari, Mohammad Saadi & Saeidian, Bahram, 2023. "Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 95-135.

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