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. © 2003 IEEE.
CITATION STYLE
Willmes, L., Back, T., Jin, Y., & Sendhoff, B. (2003). Comparing neural networks and Kriging for fitness approximation in evolutionary optimization. In 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings (Vol. 1, pp. 663–670). IEEE Computer Society. https://doi.org/10.1109/CEC.2003.1299639
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