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Comparing neural networks and Kriging for fitness approximation in evolutionary optimization.

Willmes, L, Bäck, T, Jin, Y and Sendhoff, B (2003) Comparing neural networks and Kriging for fitness approximation in evolutionary optimization.

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Neural networks and Kriging method are compared for constructing fitness approximation models in evolutionary optimization algorithms. The two models are applied in an identical framework to the optimization of a number of well known test functions. In addition, two different ways of training the approximators are evaluated: in one setting the models are built off-line using data from previous optimization runs and in the other setting the models are built online from the data available from the current optimization.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Willmes, L
Bäck, T
Jin, Y
Sendhoff, B
Date : 2003
DOI : 10.1109/CEC.2003.1299639
Contributors :
Related URLs :
Additional Information : © 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 13 Jul 2012 10:19
Last Modified : 31 Oct 2017 14:35

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