Tuning Metaheuristics

A Machine Learning Perspective

  • Mauro Birattari

Part of the Studies in Computational Intelligence book series (SCI, volume 197)

Table of contents

  1. Front Matter
  2. Mauro Birattari
    Pages 1-10
  3. Mauro Birattari
    Pages 11-67
  4. Mauro Birattari
    Pages 69-83
  5. Mauro Birattari
    Pages 85-115
  6. Mauro Birattari
    Pages 117-169
  7. Mauro Birattari
    Pages 197-201
  8. Back Matter

About this book

Introduction

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.

Keywords

Computational Intelligence Machine Learning Metaheuristics algorithm algorithms heuristics knowledge learning metaheuristic

Authors and affiliations

  • Mauro Birattari
    • 1
  1. 1.Chercheur qualifié du F.R.S.-FNRS, IRIDIA, CoDE, FSA - CP 194/6Université Libre de BruxellesBrusselsBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-00483-4
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-00482-7
  • Online ISBN 978-3-642-00483-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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