G-SELC: Optimization by Sequential Elimination of Level

Event Details
  • Date/Time:
    • Thursday October 9, 2008
      11:00 am - 12:00 pm
  • Location: Executive Classroom 228
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    $0.00
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Contact
Anita Race
H. Milton Stewart School of Industrial and Systems Engineering
Contact Anita Race
Summaries

Summary Sentence: G-SELC: Optimization by Sequential Elimination of Level

Full Summary: G-SELC: Optimization by Sequential Elimination of Level Combinations using Genetic Algorithms and Gaussian Processes

TITLE: G-SELC: Optimization by Sequential Elimination of Level Combinations using Genetic Algorithms and Gaussian Processes

SPEAKER: Dr. Abhyuday Mandal
Department of Statistics
University of Georgia

ABSTRACT:

Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in 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 (2006) 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 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.

(Joint research with C.F. Jeff Wu and Pritam Ranjan)

Additional Information

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

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Categories
Seminar/Lecture/Colloquium
Keywords
Feasible compounds, SELC algorithm
Status
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Oct 12, 2009 - 4:38pm
  • Last Updated: Oct 7, 2016 - 9:47pm