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.

Reactive Search Optimization: Learning While Optimizing / Battiti, R.; Brunato, M.; Mariello, Andrea. - (In corso di stampa).

Reactive Search Optimization: Learning While Optimizing

R. Battiti;M. Brunato;Mariello, Andrea
In corso di stampa

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.
In corso di stampa
Handbook of Metaheuristics
New York; Dordrecht; Heidelberg; London
Springer Science+Business Media, LLC
Battiti, R.; Brunato, M.; Mariello, Andrea
Reactive Search Optimization: Learning While Optimizing / Battiti, R.; Brunato, M.; Mariello, Andrea. - (In corso di stampa).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/195426
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