Please use this identifier to cite or link to this item:
|Title:||Performance of Kriging and Cokriging based surrogate models within the unified framework for surrogate assisted optimization|
|Authors:||Won, K.S. |
|Citation:||Won, K.S.,Ray, T. (2004). Performance of Kriging and Cokriging based surrogate models within the unified framework for surrogate assisted optimization. Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 2 : 1577-1585. ScholarBank@NUS Repository.|
|Abstract:||In this paper, we report the behavior of Kriging and Cokriging based surrogate models within the optimization framework. The framework is built upon a stochastic, zero order, population-based optimization algorithm embedded with controlled elitism to ensure convergence in the actual function space. The model accuracy is maintained via periodic retraining and the number of data points required to create the surrogate model is adaptively identified using Calinski Harabasz (CH) index. Results of Kriging and Cokriging are compared with Radial Basis Function models on a set of numerical and engineering design optimization problems.|
|Source Title:||Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.