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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006
eBook Chapter
38 A Novel Evolutionary Algorithm and Its Application in Bioinformatics
By
Hualin Wang
,
Hualin Wang
School of Electrical and Computer Engineering 465 Northwestern Ave.
Purdue University
West Lafayette, IN 47907
; hualin@purdue.edu
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Okan K. Ersoy
Okan K. Ersoy
School of Electrical and Computer Engineering 465 Northwestern Ave.
Purdue University
West Lafayette, IN 47907
; ersoy@purdue.edu
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Page Count:
7
-
Published:2006
Citation
Wang, H, & Ersoy, OK. "A Novel Evolutionary Algorithm and Its Application in Bioinformatics." Intelligent Engineering Systems through Artificial Neural Networks, Volume 16. Ed. Dagli, CH, Buczak, AL, Enke, DL, Embrechts, M, & Ersoy, O. ASME Press, 2006.
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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...
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
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