Title :
Learning To Optimize Constrained Problems
Author :
Jayadeva ; Shah, Sameena ; Chandra, Suresh
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi
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;
Conference_Titel :
India Conference, 2006 Annual IEEE
Conference_Location :
New Delhi
Print_ISBN :
1-4244-0369-3
Electronic_ISBN :
1-4244-0370-7
DOI :
10.1109/INDCON.2006.302858
