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.

• Type: Conference
• Year: 2003
• Where: CEC
• Authors: Lars Willmes, Thomas Bäck, Yaochu Jin, Bernhard Sendhoff
• Keywords: Artificial Intelligence, CEC 2003, Evolutionary Optimization Algorithms, Identical Framework, £tness Approximation Models