Local function approximation in evolutionary algorithms for the optimization of costly functions
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[1] Lawrence J. Fogel,et al. Toward Inductive Inference Automata , 1962, IFIP Congress.
[2] John H. Holland,et al. Outline for a Logical Theory of Adaptive Systems , 1962, JACM.
[3] Lawrence J. Fogel,et al. Artificial Intelligence through Simulated Evolution , 1966 .
[4] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[5] V. Barnett,et al. Applied Linear Statistical Models , 1975 .
[6] Dipl. Ing. Karl Heinz Kellermayer. NUMERISCHE OPTIMIERUNG VON COMPUTER-MODELLEN MITTELS DER EVOLUTIONSSTRATEGIE Hans-Paul Schwefel Birkhäuser, Basel and Stuttgart, 1977 370 pages Hardback SF/48 ISBN 3-7643-0876-1 , 1977 .
[7] D. Ackley. A connectionist machine for genetic hillclimbing , 1987 .
[8] Derek J. Pike,et al. Empirical Model‐building and Response Surfaces. , 1988 .
[9] C. Shoemaker,et al. Dynamic optimal control for groundwater remediation with flexible management periods , 1992 .
[10] Douglas C. Montgomery,et al. Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .
[11] T. J. Mitchell,et al. Exploratory designs for computational experiments , 1995 .
[12] David B. Fogel,et al. Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .
[13] R. H. Myers,et al. Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .
[14] Thomas Bäck,et al. Evolutionary computation: an overview , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.
[15] S. Batill,et al. Concurrent Subspace Optimization using gradient-enhanced neural network approximations , 1996 .
[16] John E. Renaud,et al. Response surface based, concurrent subspace optimization for multidisciplinary system design , 1996 .
[17] M. Matsunami,et al. An optimization method based on radial basis function , 1997 .
[18] S. Varadarajan,et al. Integration of design of experiments and artificial neural networks for achieving affordable concurrent design , 1997 .
[19] Thomas Bäck,et al. Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..
[20] D. Fogel. Evolutionary algorithms in theory and practice , 1997, Complex..
[21] Timothy M. Mauery,et al. COMPARISON OF RESPONSE SURFACE AND KRIGING MODELS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION , 1998 .
[22] Alain Ratle,et al. Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation , 1998, PPSN.
[23] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[24] Andy J. Keane,et al. Metamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations , 1999, GECCO.
[25] A. Ratle. Optimal sampling strategies for learning a fitness model , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[26] M. Powell. Recent research at Cambridge on radial basis functions , 1999 .
[27] M. Matsunami,et al. A combined method for the global optimization using radial basis function and deterministic approach , 1999 .
[28] Christine A. Shoemaker,et al. Comparison of Optimization Methods for Ground-Water Bioremediation , 1999 .
[29] Kenny Q. Ye,et al. Algorithmic construction of optimal symmetric Latin hypercube designs , 2000 .
[30] Bernhard Sendhoff,et al. On Evolutionary Optimization with Approximate Fitness Functions , 2000, GECCO.
[31] K. Rasheed,et al. An incremental-approximate-clustering approach for developing dynamic reduced models for design optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).
[32] S. Batill,et al. AN ITERATIVE CONCURRENT SUBSPACE ROBUST DESIGN FRAMEWORK , 2000 .
[33] Weiyu Liu,et al. GRADIENT-ENHANCED NEURAL NETWORK RESPONSE SURFACE APPROXIMATIONS , 2000 .
[34] 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.
[35] Mattias Björkman,et al. Global Optimization of Costly Nonconvex Functions Using Radial Basis Functions , 2000 .
[36] Yaochu Jin,et al. Managing approximate models in evolutionary aerodynamic design optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).
[37] Hans-Martin Gutmann,et al. On the Semi-norm of Radial Basis Function Interpolants , 2001, J. Approx. Theory.
[38] Donald R. Jones,et al. A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..
[39] Hans-Martin Gutmann,et al. A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..
[40] Andy J. Keane,et al. Evolutionary optimization for computationally expensive problems using Gaussian processes , 2001 .
[41] Khaled Rasheed,et al. Comparison Of Methods For Using Reduced Models To Speed Up Design Optimization , 2002, GECCO.
[42] Bernhard Sendhoff,et al. Fitness Approximation In Evolutionary Computation - a Survey , 2002, GECCO.
[43] Bernhard Sendhoff,et al. A framework for evolutionary optimization with approximate fitness functions , 2002, IEEE Trans. Evol. Comput..
[44] Thomas J. Santner,et al. Design and analysis of computer experiments , 1998 .
[45] Khaled Rasheed,et al. Comparison of methods for developing dynamic reduced models for design optimization , 2002, Soft Comput..
[46] Yaochu Jin,et al. A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..