DocumentCode
3401623
Title
Learning To Optimize Constrained Problems
Author
Jayadeva ; Shah, Sameena ; Chandra, Suresh
Author_Institution
Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi
fYear
2006
fDate
15-17 Sept. 2006
Firstpage
1
Lastpage
4
Abstract
This paper contains early work on how GOSAM, a learning based unconstrained optimization technique that we had proposed in previous work, can be extended to the constrained optimization domain. The algorithm, termed as a global optimizer using support vector regression based adaptive multistart (GOSAM), yielded highly encouraging results for unconstrained benchmark optimization problems
Keywords
optimisation; regression analysis; support vector machines; GOSAM; adaptive multistart; constrained optimization learning; global optimizer; support vector regression; unconstrained benchmark optimization problem; Benchmark testing; Computational modeling; Constraint optimization; Genetic algorithms; Mathematical programming; Mathematics; Minimization methods; Optimization methods; Simulated annealing; Space technology; Constrained optimization; Global optimization; Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
India Conference, 2006 Annual IEEE
Conference_Location
New Delhi
Print_ISBN
1-4244-0369-3
Electronic_ISBN
1-4244-0370-7
Type
conf
DOI
10.1109/INDCON.2006.302858
Filename
4086329
Link To Document