Reactive search optimization: Learning while optimizing

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.