Reactive search: machine learning for memory-based heuristics (2005)
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| Venue: | Teofilo F. Gonzalez (Ed.), Approximation Algorithms and Metaheuristics, Taylor & Francis Books (CRC Press |
| Citations: | 13 - 5 self |
BibTeX
@TECHREPORT{Battiti05reactivesearch:,
author = {Roberto Battiti and Mauro Brunato},
title = {Reactive search: machine learning for memory-based heuristics},
institution = {Teofilo F. Gonzalez (Ed.), Approximation Algorithms and Metaheuristics, Taylor & Francis Books (CRC Press},
year = {2005}
}
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Abstract
1 Introduction: the role of the user in 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
