Skip to Main Content
Skip Nav Destination
ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
Editor
Cihan H. Dagli
Cihan H. Dagli
Search for other works by this author on:
Anna L. Buczak
Anna L. Buczak
Search for other works by this author on:
David L. Enke
David L. Enke
Search for other works by this author on:
Mark Embrechts
Mark Embrechts
Search for other works by this author on:
Okan Ersoy
Okan Ersoy
Search for other works by this author on:
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

Gray Code Optimization (GCO) algorithm is a deterministic algorithm based on the Gray code representation. It sometimes suffers from slow convergence and sub-optimal solutions. Expectation Maximization (EM) algorithm is used to analyze how the GCO explores the search space. The results indicate that it is similar to generating samples with a mixture Gaussian distribution. Based on these findings, a novel stochastic optimization algorithm based on the mixture Gaussian model is proposed. The new algorithm is applied to molecule conformation search. Obtaining global minimum energy conformations of molecule is a very hard optimization problem. The difficulty arises from the following two factors: the conformational space of a reasonable size molecular is very large, and there are many local minima that are hard to sample efficiently. The energy landscape in the conformational space is very rugged, and there are many large barriers between local minima.

Abstract
1 Introduction
2 Using EM Algorithm to Model GCO Search Space
3 Mixture Gaussian Optimization (MGO) Algorithm
4 Experimental Results
5 Applied in Molecule Conformation Search
6 Summaries and Future Research
References
Appendix
This content is only available via PDF.
You do not currently have access to this chapter.
Close Modal

or Create an Account

Close Modal
Close Modal