Titleg-SELC: OPTIMIZATION BY SEQUENTIAL ELIMINATION OF LEVEL COMBINATIONS USING GENETIC ALGORITHMS AND GAUSSIAN PROCESSES
Publication TypeJournal Article
Year of Publication2009
AuthorsMandal, A, Ranjan, P, Wu, JCF
JournalANNALS OF APPLIED STATISTICS
Volume3
Pagination398-421
Date PublishedMAR
Type of ArticleArticle
ISSN1932-6157
Keywordsbatch-sequential design, expected improvement function, Kriging, Process optimization
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 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.

DOI10.1214/08-AOAS199
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