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The normal score transformation applied to a multi-univariate method of global optimization

✍ Scribed by Cary D. Perttunen; Bruce E. Stuckman

Publisher
Springer US
Year
1992
Tongue
English
Weight
441 KB
Volume
2
Category
Article
ISSN
0925-5001

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✦ Synopsis

Nonparametric global optimization methods have been developed that determine the location of their next guess based on the rank-transformed objective function evaluations rather than the actual function values themselves. Another commonly-used transformation in nonparametric statistics is the normal score transformation. This paper applies the normal score transformation to the multi-univariate method of global optimization. The benefits of the new method are shown by its performance on a standard set of global optimization test problems. The normal score transformation yields a method that gives equivalent searches for any monotonic transformation of the objective function.

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