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Classical and Nonclassical Optimization Methods


  1. Ron Wehrens,
  2. Lutgarde M.C. Buydens

Published Online: 15 SEP 2006

DOI: 10.1002/9780470027318.a5203

Encyclopedia of Analytical Chemistry

Encyclopedia of Analytical Chemistry

How to Cite

Wehrens, R. and Buydens, L. M. 2006. Classical and Nonclassical Optimization Methods. Encyclopedia of Analytical Chemistry. .

Author Information

  1. University of Nijmegen, The Netherlands

Publication History

  1. Published Online: 15 SEP 2006

This is not the most recent version of the article. View current version (19 JUN 2017)


Optimization problems are abundant in analytical chemistry, examples being the determination of optimal conditions for experiments or optimal settings for instruments. In general, all the required information should be obtained from as few experiments as possible. Classical techniques such as response surface models or simplex optimization are often used. These techniques, which can be very efficient in cases where the underlying assumptions are fulfilled, are called “strong” methods.

With the advent of the computer in the laboratory, a new class of optimization problems arose which could not be tackled with the standard methodologies. For these search-type problems, new strategies such as simulated annealing (SA) and genetic algorithms (GA) are applied. Although these are not guaranteed to give the optimal result, in almost all cases they are able to find very good solutions where other techniques fail completely. These methods find themselves in an intermediate position between the strong methods, mentioned above, and weak methods, where hardly any assumptions are made.

This article provides an overview of classical and nonclassical optimization techniques, and stresses the differences in their areas of application. The key ideas are highlighted and references to important publications are given.