Reactive Search Optimization: Learning While Optimizing

Roberto Battiti (), Mauro Brunato () and Andrea Mariello ()
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Roberto Battiti: University of Trento
Mauro Brunato: University of Trento
Andrea Mariello: University of Trento

Chapter Chapter 15 in Handbook of Metaheuristics, 2019, pp 479-511 from Springer

Abstract: 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.

Keywords: Reactive Search Optimization (RSO); Reactive Tabu Search Algorithm; Vehicle Routing Problem With Time Windows (VRPTW); Iterated Local Search (ILS); Stochastic Local Search (SLS) (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-319-91086-4_15

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