Tuning Metaheuristics

A Machine Learning Perspective
Softcover Nachdruck of hardcover 1. Auflage 2009
ISBN: 978-3-642-10149-6
Verlag: Springer

Buch, Englisch, Band 197, 221 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 359 g

Reihe: Studies in Computational Intelligence

Metaheuristics are a relatively new but already established approachto c- binatorial optimization. A metaheuristic is a generic algorithmic template that can be used for ?nding high quality solutions of hard combinatorial - timization problems. To arrive at a functioning algorithm, a metaheuristic needs to be con?gured: typically some modules need to be instantiated and someparametersneedto betuned.Icallthese twoproblems"structural"and "parametric" tuning, respectively. More generally, I refer to the combination of the two problems as "tuning". Tuning is crucial to metaheuristic optimization both in academic research andforpracticalapplications.Nevertheless,relativelylittle researchhasbeen devoted to the issue. This book shows that the problem of tuning a me- heuristic can be described and solved as a machine learning problem. Using the machine learning perspective, it is possible to give a formal de?nitionofthetuningproblemandtodevelopagenericalgorithmfortuning metaheuristics.Moreover,fromthemachinelearningperspectiveitispossible tohighlightsome?awsinthecurrentresearchmethodologyandtostatesome guidelines for future empirical analysis in metaheuristics research. This book is based on my doctoral dissertation and contains results I have obtained starting from 2001 while working within the Metaheuristics Net- 1 work. During these years I have been a?liated with two research groups: INTELLEKTIK, Technische Universität Darmstadt, Darmstadt, Germany and IRIDIA, Université Libre de Bruxelles, Brussels, Belgium. I am the- fore grateful to the research directors of these two groups: Prof. Wolfgang Bibel, Dr. Thomas Stützle, Prof. Philippe Smets, Prof. Hugues Bersini, and Prof. Marco Dorigo.

Zielgruppe

Research

Autoren/Hrsg.

Birattari, Mauro

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Background and State-of-the-Art.- Statement of the Tuning Problem.- F-Race for Tuning Metaheuristics.- Experiments and Applications.- Some Considerations on the Experimental Methodology.- Conclusions.