Overview
- Presents a machine learning approach to methaheuristics
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 197)
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Table of contents (7 chapters)
Keywords
About this book
The importance of tuning metaheuristics is widely acknowledged in scientific literature. However, there is very little dedicated research on the subject. Typically, scientists and practitioners tune metaheuristics by hand, guided only by their experience and by some rules of thumb. Tuning metaheuristics is often considered to be more of an art than a science.
This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning. By adopting a machine learning perspective, the author gives a formal definition of the tuning problem, develops a generic algorithm for tuning metaheuristics, and defines an appropriate experimental methodology for assessing the performance of metaheuristics.
Authors and Affiliations
Bibliographic Information
Book Title: Tuning Metaheuristics
Book Subtitle: A Machine Learning Perspective
Authors: Mauro Birattari
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-00483-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-642-00482-7Published: 08 April 2009
Softcover ISBN: 978-3-642-10149-6Published: 28 October 2010
eBook ISBN: 978-3-642-00483-4Published: 02 May 2009
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 221
Additional Information: Originally published by IOS Press, 2005
Topics: Applications of Mathematics, Mathematical and Computational Engineering, Artificial Intelligence