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CEC
2003
IEEE

Comparing neural networks and Kriging for fitness approximation in evolutionary optimization

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Comparing neural networks and Kriging for fitness approximation in evolutionary optimization
Neural networks and the Kriging method are compared for constructing £tness 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.
Lars Willmes, Thomas Bäck, Yaochu Jin, Bernha
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where CEC
Authors Lars Willmes, Thomas Bäck, Yaochu Jin, Bernhard Sendhoff
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