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Introduction

  • Thomas Bartz-BeielsteinEmail author
  • Marco ChiarandiniEmail author
  • Luís PaqueteEmail author
  • Mike PreussEmail author
Chapter

Abstract

Theory and experiments are complementary ways to analyze optimization algorithms. Experiments can also live a life of their own and produce learning without need to follow or test a theory. Yet, in order to make conclusions based on experiments trustworthy, reliable, and objective a systematic methodology is needed. In the natural sciences, this methodology relies on the mathematical framework of statistics. This book collects the results of recent research that focused on the application of statistical principles to the specific task of analyzing optimization algorithms.

Keywords

Multiobjective Optimization Solution Quality Algorithm Performance Vehicle Route Problem Biobjective Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Institute of Computer ScienceCologne University of Applied SciencesGummersbachGermany
  2. 2.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdenseDenmark
  3. 3.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  4. 4.Algorithm Engineering, TU DortmundDortmundGermany

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