Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Sequential modeling of a low noise amplifier with neural networks and active learning
Neural Computing and Applications
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
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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 multi-objective approach gives information about output correlation and facilitates the generation of diverse ensembles. The merit of this approach is illustrated with a modeling problem from aerospace.