Abstract
When dealing with computationally expensive simulation codes or process measurement data, global surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, and splines. In addition, the cost of each simulation mandates the use of active learning strategies where data points (simulations) are selected intelligently and incrementally. When applying surrogate models to multi-output systems, the hyperparameter optimization problem is typically formulated in a single objective way. The different response outputs are modeled separately by independent models. Instead, a multi-objective approach would benefit the domain expert by giving information about output correlation, facilitate the generation of diverse ensembles, and enable automatic model type selection for each output on the fly. This paper outlines a multi-objective approach to surrogate model generation including its application to two problems.
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References
Gorissen, D., Couckuyt, I., Dhaene, T.: Multiobjective global surrogate modeling. Technical Report TR-08-08, University of Antwerp, Middelheimlaan 1, 2020 Antwerp, Belgium (2008)
Jin, B.Y., Sendhoff: Pareto-based multiobjective machine learning: An overview and case studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(3), 397–415 (2008)
Fenicia, F., Solomatine, D.P., Savenije, H.H.G., Matgen, P.: Soft combination of local models in a multi-objective framework. Hydrology and Earth System Sciences Discussions 4(1), 91–123 (2007)
Gorissen, D., De Tommasi, L., Croon, J., Dhaene, T.: Automatic model type selection with heterogeneous evolution: An application to rf circuit block modeling. In: Proceedings of the IEEE Congress on Evolutionary Computation, WCCI 2008, Hong Kong(2008)
Mierswa, I.: Controlling overfitting with multi-objective support vector machines. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1830–1837. ACM Press, New York (2007)
Fieldsend, J.E.: Multi-objective supervised learning. In: Knowles, J., Corne, D., Deb, K. (eds.) Multiobjective Problem Solving from Nature From Concepts to Applications. Natural Computing Series. LNCS. Springer, Heidelberg (2008)
Knowles, J.: Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation 10(1), 50–66 (2006)
Voutchkov, I., Keane, A.: Multiobjective Optimization using Surrogates. In: Parmee, I. (ed.) Adaptive Computing in Design and Manufacture 2006. Proceedings of the Seventh International Conference, Bristol, UK, pp. 167–175 (April 2006)
Keane, A.J.: Statistical improvement criteria for use in multiobjective design optimization. AIAA Journal 44(4), 879–891 (2006)
Knowles, J.D., Nakayama, H.: Meta-modeling in multiobjective optimization. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 245–284. Springer, Heidelberg (2008)
Last, M.: Multi-objective classification with info-fuzzy networks. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS, vol. 3201, pp. 239–249. Springer, Heidelberg (2004)
Keys, A.C., Rees, L.P., Greenwood, A.G.: Performance measures for selection of metamodels to be used in simulation optimization. Decision Sciences 33, 31–58 (2007)
Lee, T.: The Design of CMOS Radio-Frequency Integrated Circuits, 2nd edn. Cambridge University Press, Cambridge (2003)
Gorissen, D., De Tommasi, L., Crombecq, K., Dhaene, T.: Sequential modeling of a low noise amplifier with neural networks and active learning. Neural Computing and Applications 18(5), 485–494 (2009)
Lophaven, S.N., Nielsen, H.B., Søndergaard, J.: Aspects of the matlab toolbox DACE. Technical report, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby (2002)
Gorissen, D., De Tommasi, L., Hendrickx, W., Croon, J., Dhaene, T.: Rf circuit block modeling via kriging surrogates. In: Proceedings of the 17th International Conference on Microwaves, Radar and Wireless Communications, MIKON 2008 (2008)
Nørgaard, M., Ravn, O., Hansen, L., Poulsen, N.: The NNSYSID toolbox. In: IEEE International Symposium on Computer-Aided Control Sysstems Design (CACSD), Dearborn, Michigan, USA, pp. 374–379 (1996)
Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishing Co., Pte, Ltd., Singapore (2002)
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Gorissen, D., Couckuyt, I., Laermans, E., Dhaene, T. (2009). Pareto-Based Multi-output Metamodeling with Active Learning. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_36
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DOI: https://doi.org/10.1007/978-3-642-03969-0_36
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