2010 | OriginalPaper | Buchkapitel
Reactive Search Optimization: Learning While Optimizing
verfasst von : Roberto Battiti, Mauro Brunato
Erschienen in: Handbook of Metaheuristics
Verlag: Springer US
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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).