Design and analysis of computational experiments: Overview

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

6 Citations (Scopus)

Abstract

This chapter presents an overview of the design and analysis of computational experiments with optimization algorithms. It covers classic designs and their corresponding (meta)models; namely, Resolution-III designs including fractional factorial two-level designs for first-order polynomial models, Resolution-IV and Resolution-V designs for two-factor interactions, and designs including central composite designs for second-degree polynomials. It also reviews factor screening in experiments with very many factors, focusing on the sequential bifurcation method. Furthermore, it reviews Kriging models and their designs. Finally, it discusses experiments aimed at the optimization of the parameters of a given optimization algorithm, allowing multiple random experimental outputs. This optimization may use either generalized response surface methodology or Kriging combined with mathematical programming; the discussion also covers Taguchian robust optimization.
Original languageEnglish
Title of host publicationEmpirical Methods for the Analysis of Optimization Algorithms
EditorsT. Bartz-Beielstein, M. Chiarandini, L. Paquete, M. Preuss
Place of PublicationHeidelberg
PublisherSpringer Verlag
Pages51-72
ISBN (Print)9783642025372
Publication statusPublished - 2010

Fingerprint Dive into the research topics of 'Design and analysis of computational experiments: Overview'. Together they form a unique fingerprint.

  • Cite this

    Kleijnen, J. P. C. (2010). Design and analysis of computational experiments: Overview. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, & M. Preuss (Eds.), Empirical Methods for the Analysis of Optimization Algorithms (pp. 51-72). Springer Verlag.