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March 2009 $\mathcal{G}$-SELC: Optimization by sequential elimination of level combinations using genetic algorithms and Gaussian processes
Abhyuday Mandal, Pritam Ranjan, C. F. Jeff Wu
Ann. Appl. Stat. 3(1): 398-421 (March 2009). DOI: 10.1214/08-AOAS199

Abstract

Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in the pharmaceutical industry. An efficient solution to this problem will help reduce the expenditure at the early stages of drug discovery. In an attempt to solve this problem, Mandal, Wu and Johnson [Technometrics 48(2006) 273–283] proposed the SELC algorithm. Although powerful, it fails to extract substantial information from the data to guide the search efficiently, as this methodology is not based on any statistical modeling. The proposed approach uses Gaussian Process (GP) modeling to improve upon SELC, and hence named $\mathcal{G}$-SELC. The performance of the proposed methodology is illustrated using four and five dimensional test functions. Finally, we implement the new algorithm on a real pharmaceutical data set for finding a group of chemical compounds with optimal properties.

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Abhyuday Mandal. Pritam Ranjan. C. F. Jeff Wu. "$\mathcal{G}$-SELC: Optimization by sequential elimination of level combinations using genetic algorithms and Gaussian processes." Ann. Appl. Stat. 3 (1) 398 - 421, March 2009. https://doi.org/10.1214/08-AOAS199

Information

Published: March 2009
First available in Project Euclid: 16 April 2009

zbMATH: 1165.62093
MathSciNet: MR2668713
Digital Object Identifier: 10.1214/08-AOAS199

Keywords: batch-sequential design , expected improvement function , kriging , Process optimization

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.3 • No. 1 • March 2009
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