Pareto-Based Multi-output Model Type Selection

In engineering design the use of approximation models (= surrogate models) has become standard practice for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, Kriging models, and splines. An engineering simulation typically involves multiple response variables that must be approximated. With many approximation methods available, the question of which method to use for which response consistently arises among engineers and domain experts. Traditionally, the different responses are modeled separately by independent models, possibly involving a comparison among model types. Instead, this paper proposes a multi-objective approach can benefit the domain expert since it enables automatic model type selection for each output on the fly without resorting to multiple runs. In effect the optimal model complexity and model type for each output is determined automatically. In addition a m...

• Type: Conference
• Year: 2009
• Where: HAIS
• Authors: Dirk Gorissen, Ivo Couckuyt, Karel Crombecq, Tom Dhaene
• Keywords: Artificial Intelligence, HAIS 2009, Model Type, Surrogate Model, Surrogate Model Types