Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
G. Cybenko. Approximation by superposition of a sigmoidal function. Math. Control Signals Systems, 2:303-314, 1989.
K. Foli, M. Olhofer T. Okabe, Y. Jin, and B. Sendhoff. Optimization of micro heat exchanger: CFD, analytical approach and multi-objective evolutionary algorithms. International Journal of Heat and Mass Transfer, 49:1090-1099, 2006.
L. Gräning, Y. Jin, and B. Sendhoff. Efficient evolutionary optimization using individual-based evolution control and neural networks: A comparative study. In European Symposium on Artificial Neural Networks, pages 273-278, 2005.
N. Hansen and S. Kern. Evaluating the cma evolution strategy on multimodal test functions. In Eight International Conference on Parallel Poblem Solving from Nature PPSN VIII, pages 282-291. Springer, 2004.
M. Hasenjäger, B. Sendhoff, T. Sonoda, and T. Arima. Three dimensional aerodynamic optimization for an ultra-low aspect ratio transonic turbine stator blade. In ASME Turbo Expo, 2005.
K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5):359-366, 1989.
M. Hüsken, Y. Jin, and B. Sendhoff. Structure optimization of neural networks for evolutionary design optimization. In GECCO Workshop on Approximation and Learning in Evolutionary Computation, pages 13-16, 2002.
Y. Jin. A comprehensive survey of fitness approximation in evolutionary comsputation. Soft Computing, 9(1):3-12, 2005.
Y. Jin, M. Huesken, and B. Sendhoff. Quality measures for approximate models in evolutionary computation. In Proceedings of GECCO Workshops: Workshop on Adaptation, Learning and Approximation in Evolutionary Computation, pages 170-174, Chicago, 2003.
Y. Jin, M. Olhofer, and B. Sendhoff. On evolutionary optimization with approximate fitness functions. In Genetic and Evolutionary Computation Conference, pages 786-792. Morgan Kaufmann, 2000.
Y. Jin, M. Olhofer, and B. Sendhoff. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6(5):481-494, 2002.
Y. Jin and B. Sendhoff. Reducing fitness evaluations using clustering techniques and neural networks ensembles. In Genetic and Evolutionary Computation Conference, volume 3102 of LNCS, pages 688-699. Springer, 2004.
M. Olhofer, T. Arima, Y. Jin, T. Sonoda, and B. Senhoff. Optimisation of transonic gas turbine blades with evolution strategies. Honda Technical Reviews, pages 203-216, April 2002. documents/HTR02.pdf.
M. Olhofer, T. Arima, T. Sonoda, and B. Sendhoff. Optimisation of a stator blade used in a transonic compressor cascade with evolution strategies. In Adaptive Computation in Design and Manufacture, pages 45-54. Springer, 2000.
Y.S. Ong, P.B. Nair, and A.J. Keane. Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA Journal, 41(4):687- 696,2003.
Y.S. Ong, P.B. Nair, A.J. Keane, and K.W. Wong. Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems. In Y. Jin, editor, Knowledge Incorporation in Evolutionary Computation, pages 307-331. Springer, 2005.
A. Oyama. Multidisciplinary optimization of transonic wing design based on evolutionary algorithms coupled with cfd solver. In European Congress on Computational Methods in Applied Science and Engineering, ECCOMAS 2000, 2000.
M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: The rprop algorithm. IEEE Int. Conference on Neural Networks, pages 586-591, 1993.
H. Ulmer, F. Streichert, and A. Zell. Evolution strategies with controlled model assistance. In Congress on Evolutionary Computation, pages 1569-1576, 2004.
Cheng Xiang. Geometrical interpretation and architecture selection of MLP. IEEE Transaction on Neural Networks, 16(1):84-96, 2005.
X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447, 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Gräning, L., Jin, Y., Sendhoff, B. (2007). Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization. In: Yang, S., Ong, YS., Jin, Y. (eds) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49774-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-49774-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49772-1
Online ISBN: 978-3-540-49774-5
eBook Packages: EngineeringEngineering (R0)