Reactive Search Optimization: Learning While Optimizing

Roberto Battiti () and Mauro Brunato ()
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Roberto Battiti: LION Lab, Università di Trento
Mauro Brunato: LION Lab, Università di Trento

Chapter Chapter 18 in Handbook of Metaheuristics, 2010, pp 543-571 from Springer

Abstract: 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).

Keywords: Local Search; Tabu Search; Variable Neighborhood Search; Vehicle Route Problem; Iterate Local Search (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-1665-5_18

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