Reactive Search: Machine Learning for Memory-Based Heuristics

Most state-of-the-art heuristics are characterized by a certain number of choices and free parameters, whose appropriate setting is a subject that raises issues of research methodology [5, 41, 51]. In some cases, these parameters are tuned through a feedback loop that includes the user as a crucial learning component : depending on preliminary algorithm tests some parameter values are changed by the user, and different options are tested until acceptable results are obtained. Therefore, the quality of results is not automatically transferred to different instances and the feedback loop can require a lengthy “trial and error” process every time the algorithm has to be tuned for a new application. Parameter tuning is therefore a crucial issue both in the scientific development and in the practical use of heuristics. In some cases the role of the user as an intelligent (learning) part makes the reproducibility of heuristic results difficult and, as a consequence, the competitiveness of alternative techniques depends in a crucial way on the user’s capabilities. Work supported by the project BIONETS (IST-027748) funded by the FET Program of the European Commission. To be published as Chapter 21 of Teófilo Gonzalez (editor) Approximation Algorithms and Metaheuristics, Taylor&Francis, 2007.