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).
CITATION STYLE
Battiti, R., & Brunato, M. (2010). Reactive Search Optimization: Learning While Optimizing (pp. 543–571). https://doi.org/10.1007/978-1-4419-1665-5_18
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