Adapting control policies for expensive systems to changing environments
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[1] Kenneth Holmström,et al. An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization , 2008, J. Glob. Optim..
[2] D. Dennis,et al. A statistical method for global optimization , 1992, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics.
[3] Andrew W. Moore,et al. Memory-based Stochastic Optimization , 1995, NIPS.
[4] Howie Choset,et al. Using response surfaces and expected improvement to optimize snake robot gait parameters , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[5] Antanas Zilinskas,et al. A review of statistical models for global optimization , 1992, J. Glob. Optim..
[6] Howie Choset,et al. Parameterized and Scripted Gaits for Modular Snake Robots , 2009, Adv. Robotics.
[7] Christine A. Shoemaker,et al. A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions , 2007, INFORMS J. Comput..
[8] Thomas J. Santner,et al. Sequential design of computer experiments for robust parameter design , 2002 .
[9] Harold J. Kushner,et al. A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise , 1964 .
[10] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[11] Trevor Darrell,et al. Active Learning with Gaussian Processes for Object Categorization , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[12] Richard J. Beckman,et al. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.
[13] Hans-Martin Gutmann,et al. A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..
[14] Eric Walter,et al. Global optimization based on noisy evaluations: An empirical study of two statistical approaches , 2008 .
[15] Howie Choset,et al. Design of a modular snake robot , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[16] Donald R. Jones,et al. A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..
[17] M. Emmerich,et al. The computation of the expected improvement in dominated hypervolume of Pareto front approximations , 2008 .
[18] N. Zheng,et al. Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models , 2006, J. Glob. Optim..
[19] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[20] David Ginsbourger,et al. Noisy Expected Improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity , 2010 .
[21] H. Zimmermann. Towards global optimization 2: L.C.W. DIXON and G.P. SZEGÖ (eds.) North-Holland, Amsterdam, 1978, viii + 364 pages, US $ 44.50, Dfl. 100,-. , 1979 .
[22] Thomas J. Santner,et al. Sequential design of computer experiments to minimize integrated response functions , 2000 .
[23] Joshua D. Knowles,et al. ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.
[24] George E. P. Box,et al. Empirical Model‐Building and Response Surfaces , 1988 .