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).

Reactive Search Optimization: Learning While Optimizing

Battiti, Roberto;Brunato, Mauro
2010-01-01

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).
2010
Handbook of Metaheuristics
Berlin; Heidelberg
Springer
9781441916631
Battiti, Roberto; Brunato, Mauro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/85128
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