iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.crossref.org/works/10.1007/978-3-642-01082-8_2
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T10:18:20Z","timestamp":1725531500923},"publisher-location":"Berlin, Heidelberg","reference-count":70,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642010811"},{"type":"electronic","value":"9783642010828"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009]]},"DOI":"10.1007\/978-3-642-01082-8_2","type":"book-chapter","created":{"date-parts":[[2009,5,4]],"date-time":"2009-05-04T16:35:56Z","timestamp":1241454956000},"page":"35-62","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Approximation of Expensive Functions with Active Learning"],"prefix":"10.1007","author":[{"given":"Dirk","family":"Gorissen","sequence":"first","affiliation":[]},{"given":"Karel","family":"Crombecq","sequence":"additional","affiliation":[]},{"given":"Ivo","family":"Couckuyt","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Dhaene","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"issue":"4","key":"2_CR1","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1115\/1.2429697","volume":"129","author":"G.G. Wang","year":"2007","unstructured":"Wang, G.G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design\u00a0129(4), 370\u2013380 (2007)","journal-title":"Journal of Mechanical Design"},{"doi-asserted-by":"crossref","unstructured":"Gu, L.: A comparison of polynomial based regression models in vehicle safety analysis. In: Diaz, A. (ed.) 2001 ASME Design Automation Conference, ASME, Pittsburgh, PA (2001)","key":"2_CR2","DOI":"10.1115\/DETC2001\/DAC-21063"},{"key":"2_CR3","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/15732470600590507","volume":"2","author":"A. Giunta","year":"2006","unstructured":"Giunta, A., McFarland, J., Swiler, L., Eldred, M.: The promise and peril of uncertainty quantification using response surface approximations. Structure & Infrastructure Engineering\u00a02, 175\u2013189 (2006)","journal-title":"Structure & Infrastructure Engineering"},{"key":"2_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71351-7_45","volume-title":"High Performance Computing for Computational Science - VECPAR 2006","author":"D. Gorissen","year":"2007","unstructured":"Gorissen, D., Crombecq, K., Hendrickx, W., Dhaene, T.: Grid enabled metamodeling. In: Dayd\u00e9, M., Palma, J.M.L.M., Coutinho, \u00c1.L.G.A., Pacitti, E., Lopes, J.C. (eds.) VECPAR 2006. LNCS, vol.\u00a04395. Springer, Heidelberg (2007)"},{"doi-asserted-by":"crossref","unstructured":"Hendrickx, W., Dhaene, T.: Sequential design and rational metamodelling. In: Kuhl, M., Steiger, N.M., Armstrong, F.B., Joines, J.A. (eds.) Proceedings of the 2005 Winter Simulation Conference, pp. 290\u2013298 (2005)","key":"2_CR5","DOI":"10.1109\/WSC.2005.1574263"},{"issue":"7","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1109\/TMTT.2008.924346","volume":"56","author":"D. Deschrijver","year":"2008","unstructured":"Deschrijver, D., Dhaene, T., Zutter, D.D.: Robust parametric macromodeling using multivariate orthonormal vector fitting. IEEE Transactions on Microwave Theory and Techniques\u00a056(7), 1661\u20131667 (2008)","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"2","key":"2_CR7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/PL00007198","volume":"17","author":"T.W. Simpson","year":"2001","unstructured":"Simpson, T.W., Poplinski, J.D., Koch, P.N., Allen, J.K.: Metamodels for computer-based engineering design: Survey and recommendations. Eng. Comput (Lond.)\u00a017(2), 129\u2013150 (2001)","journal-title":"Eng. Comput. (Lond.)"},{"doi-asserted-by":"crossref","unstructured":"Balewski, L., Mrozowski, M.: Creating neural models using an adaptive algorithm for optimal size of neural network and training set. In: 15th International Conference on Microwaves, Radar and Wireless Communications, MIKON 2004, Conference proceedings of MIKON 2004, vol.\u00a02, pp. 543\u2013546 (2004)","key":"2_CR8","DOI":"10.1109\/MIKON.2004.1357088"},{"issue":"1","key":"2_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF01197708","volume":"17","author":"A.J. Booker","year":"1999","unstructured":"Booker, A.J., Dennis, J.E., Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W.: A rigorous framework for optimization of expensive functions by surrogate. Structural and Multidisciplinary Optimization\u00a017(1), 1\u201313 (1999)","journal-title":"Structural and Multidisciplinary Optimization"},{"doi-asserted-by":"crossref","unstructured":"Eldred, M.S., Dunlavy, D.M.: Formulations for surrogate-based optimization wiht data fit, multifidelity, and reduced-order models. In: 11th AIAA\/ISSMO Multidisciplinary Analysis and Optimization Conference, Protsmouth, Virginia (2006)","key":"2_CR10","DOI":"10.2514\/6.2006-7117"},{"key":"2_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/3-540-46084-5_217","volume-title":"Artificial Neural Networks - ICANN 2002","author":"D. Anguita","year":"2002","unstructured":"Anguita, D., Ridella, S., Rivieccio, F., Zunino, R.: Automatic hyperparameter tuning for support vector machines. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol.\u00a02415, pp. 1345\u20131350. Springer, Heidelberg (2002)"},{"unstructured":"Sanchez, E., Pintos, S., Queipo, N.: Toward an optimal ensemble of kernel-based approximations with engineering applications. In: Proceedings of the International Joint Conference on Neural Networks, 2006. IJCNN 2006, pp. 2152\u20132158 (2006)","key":"2_CR12"},{"issue":"3","key":"2_CR13","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s00366-006-0051-9","volume":"23","author":"C.J. Turner","year":"2007","unstructured":"Turner, C.J., Crawford, R.H., Campbell, M.I.: Multidimensional sequential sampling for nurbs-based metamodel development. Eng. with Comput.\u00a023(3), 155\u2013174 (2007)","journal-title":"Eng. with Comput."},{"unstructured":"Sugiyama, M., Ogawa, H.: Release from active learning\/model selection dilemma: optimizing sample points and models at the same time. In: Neural Networks, 2002. IJCNN 2002. Proceedings of the 2002 International Joint Conference on Neural Networks, 2002, vol.\u00a03, pp. 2917\u20132922 (2002)","key":"2_CR14"},{"unstructured":"Lin, Y.: An Efficient Robust Concept Exploration Method and Sequential Exploratory Experimental Design. PhD thesis, Georgia Institute of Technology (2004)","key":"2_CR15"},{"issue":"1","key":"2_CR16","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1137\/050639983","volume":"29","author":"D. Busby","year":"2007","unstructured":"Busby, D., Farmer, C.L., Iske, A.: Hierarchical nonlinear approximation for experimental design and statistical data fitting. SIAM Journal on Scientific Computing\u00a029(1), 49\u201369 (2007)","journal-title":"SIAM Journal on Scientific Computing"},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1145\/1015330.1015367","volume-title":"ICML 2004: Proceedings of the twenty-first international conference on Machine learning","author":"R.B. Gramacy","year":"2004","unstructured":"Gramacy, R.B., Lee, H.K.H., Macready, W.G.: Parameter space exploration with gaussian process trees. In: ICML 2004: Proceedings of the twenty-first international conference on Machine learning, p. 45. ACM Press, New York (2004)"},{"issue":"15","key":"2_CR18","doi-asserted-by":"publisher","first-page":"2104","DOI":"10.1002\/nme.1261","volume":"62","author":"A. Farhang-Mehr","year":"2005","unstructured":"Farhang-Mehr, A., Azarm, S.: Bayesian meta-modelling of engineering design simulations: a sequential approach with adaptation to irregularities in the response behaviour. International Journal for Numerical Methods in Engineering\u00a062(15), 2104\u20132126 (2005)","journal-title":"International Journal for Numerical Methods in Engineering"},{"issue":"7","key":"2_CR19","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMTT.2003.814318","volume":"51","author":"V. Devabhaktuni","year":"2003","unstructured":"Devabhaktuni, V., Chattaraj, B., Yagoub, M., Zhang, Q.J.: Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping. IEEE Tran. on Microwave Theory and Techniques\u00a051(7), 1822\u20131833 (2003)","journal-title":"IEEE Tran. on Microwave Theory and Techniques"},{"unstructured":"Ganser, M., Grossenbacher, K., Schutz, M., Willmes, L., Back, T.: Simulation meta-models in the early phases of the product development process. In: Proceedings of Efficient Methods for Robust Design and Optimization (EUROMECH 2007) (2007)","key":"2_CR20"},{"doi-asserted-by":"crossref","unstructured":"Clarke, S.M., Griebsch, J.H., Simpson, T.W.: Analysis of support vector regression for approximation of complex engineering analyses. In: Proceedings of the 29th Design Automation Conference (ASME Design Engineering Technical Conferences) (DAC\/DETC 2003) (2003)","key":"2_CR21","DOI":"10.1115\/DETC2003\/DAC-48759"},{"unstructured":"Gorissen, D., Crombecq, K., Couckuyt, I., Dhaene, T.: Automatic approximation of expensive functions with active learning. Technical Report TR-10-08, University of Antwerp, Middelheimlaan 1, 2020 Antwerp, Belgium (2008)","key":"2_CR22"},{"doi-asserted-by":"crossref","unstructured":"Goel, T., Haftka, R., Shyy, W.: Comparing error estimation measures for polynomial and kriging approximation of noise-free functions. Journal of Structural and Multidisciplinary Optimization (published online) (2008)","key":"2_CR23","DOI":"10.1007\/s00158-008-0290-z"},{"key":"2_CR24","first-page":"307","volume-title":"Studies in Fuzziness and Soft Computing Series","author":"Y.S. Ong","year":"2004","unstructured":"Ong, Y.S., Nair, P.B., Keane, A.J., Wong, K.W.: Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems, Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing Series, pp. 307\u2013331. Springer, Heidelberg (2004)"},{"doi-asserted-by":"crossref","unstructured":"Giunta, A., Eldred, M.: Implementation of a trust region model management strategy in the DAKOTA optimization toolkit. In: Proceedings of the 8th AIAA\/USAF\/NASA\/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Long Beach, CA (2000)","key":"2_CR25","DOI":"10.2514\/6.2000-4935"},{"issue":"4","key":"2_CR26","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"D.R. Jones","year":"1998","unstructured":"Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. of Global Optimization\u00a013(4), 455\u2013492 (1998)","journal-title":"J. of Global Optimization"},{"unstructured":"Sasena, M.J., Papalambros, P.Y., Goovaerts, P.: Metamodeling sampling criteria in a global optimization framework. In: 8th AIAA\/ USAF\/NASA\/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Long Beach, CA, AIAA Paper, 2000\u20134921 (2000)","key":"2_CR27"},{"doi-asserted-by":"crossref","unstructured":"Parmee, I., Abraham, J., Shackelford, M., Rana, O.F., Shaikhali, A.: Towards autonomous evolutionary design systems via grid-based technologies. In: Proceedings of ASCE Computing in Civil Engineering, Cancun, Mexico (2005)","key":"2_CR28","DOI":"10.1061\/40794(179)118"},{"doi-asserted-by":"crossref","unstructured":"Eldred, M., Outka, D., Fulcher, C., Bohnhoff, W.: Optimization of complex mechanics simulations with object-oriented software design. In: Proceedings of the 36th IAA\/ASME\/ASCE\/AHS\/ASC Structures, Structural Dynamics, and Materials Conference, New Orleans, LA, pp. 2406\u20132415 (1995)","key":"2_CR29","DOI":"10.2514\/6.1995-1433"},{"volume-title":"Neural Networks for RF and Microwave Design (Book + Neuromodeler Disk)","year":"2000","author":"Q.J. Zhang","unstructured":"Zhang, Q.J., Gupta, K.C.: Neural Networks for RF and Microwave Design (Book + Neuromodeler Disk). Artech House, Inc., Norwood (2000)","key":"2_CR30"},{"doi-asserted-by":"crossref","unstructured":"Gano, S., Kim, H., Brown, D.: Comparison of three surrogate modeling techniques: Datascape, kriging, and second order regression. In: Proceedings of the 11th AIAA\/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA-2006-7048, Portsmouth, Virginia (2006)","key":"2_CR31","DOI":"10.2514\/6.2006-7048"},{"unstructured":"Crombecq, K.: A gradient based approach to adaptive metamodeling. Technical report, University of Antwerp (2007)","key":"2_CR32"},{"issue":"9","key":"2_CR33","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1109\/22.788515","volume":"47","author":"J. De Geest","year":"1999","unstructured":"De Geest, J., Dhaene, T., Fach\u00e9, N., De Zutter, D.: Adaptive CAD-model building algorithm for general planar microwave structures. IEEE Transactions on Microwave Theory and Techniques\u00a047(9), 1801\u20131809 (1999)","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"key":"2_CR34","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1145\/268437.268495","volume-title":"WSC 1997: Proceedings of the 29th conference on Winter simulation","author":"R.R. Barton","year":"1997","unstructured":"Barton, R.R.: Design of experiments for fitting subsystem metamodels. In: WSC 1997: Proceedings of the 29th conference on Winter simulation, pp. 303\u2013310. ACM Press, New York (1997)"},{"key":"2_CR35","doi-asserted-by":"publisher","first-page":"2177","DOI":"10.1098\/rspa.2006.1679","volume":"462","author":"A.I.J. Forrester","year":"2006","unstructured":"Forrester, A.I.J., Bressloff, N.W., Keane, A.J.: Optimization using surrogate models and partially converged computational fluid dynamics simulations. Proceedings of the Royal Society\u00a0462, 2177\u20132204 (2006)","journal-title":"Proceedings of the Royal Society"},{"issue":"4","key":"2_CR36","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1137\/0917063","volume":"17","author":"S. Yesilyurt","year":"1996","unstructured":"Yesilyurt, S., Ghaddar, C.K., Cruz, M.E., Patera, A.T.: Bayesian-validated surrogates for noisy computer simulations; application to random media. SIAM Journal on Scientific Computing\u00a017(4), 973\u2013992 (1996)","journal-title":"SIAM Journal on Scientific Computing"},{"issue":"1","key":"2_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00137910308965049","volume":"48","author":"R. Chaveesuk","year":"2003","unstructured":"Chaveesuk, R., Smith, A.: Economic valuation of capital projects using neural network metamodels. The Engineering Economist\u00a048(1), 1\u201330 (2003)","journal-title":"The Engineering Economist"},{"key":"2_CR38","first-page":"1088","volume":"55","author":"D. Knight","year":"2004","unstructured":"Knight, D., Kohn, J., Rasheed, K., Weber, N., Kholodovych, V., Welsh, W., Smith, J.: Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces. Journal of chemical information and computer science\u00a055, 1088\u20131097 (2004)","journal-title":"Journal of chemical information and computer science"},{"key":"2_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/978-3-540-24855-2_112","volume-title":"Genetic and Evolutionary Computation \u2013 GECCO 2004","author":"D. Hidovic","year":"2004","unstructured":"Hidovic, D., Rowe, J.: Validating a model of colon colouration using an evolution strategy with adaptive approximations. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol.\u00a03103, pp. 1005\u20131016. Springer, Heidelberg (2004)"},{"unstructured":"Brown, M., Adams, S., Dunlavy, B., Gay, D., Swiler, D., Giunta, L., Hart, A., Watson, W., Eddy, J.P., Griffin, J., Hough, J., Kolda, P., Martinez-Canales, T., Eldred, M., Williams, P.: Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 4.1 users manual. Technical Report SAND2006-6337, Sandia Labs (2007)","key":"2_CR40"},{"issue":"3","key":"2_CR41","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1287\/ijoc.1050.0136","volume":"17","author":"J.P. Kleijnen","year":"2005","unstructured":"Kleijnen, J.P., Sanchez, S.M., Lucas, T.W., Cioppa, T.M.: State-of-the-art review: A user\u2019s guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing\u00a017(3), 263\u2013289 (2005)","journal-title":"INFORMS Journal on Computing"},{"doi-asserted-by":"crossref","unstructured":"Ding, M., Vemur, R.: An active learning scheme using support vector machines for analog circuit feasibility classification. In: 18th International Conference on VLSI Design, pp. 528\u2013534 (2005)","key":"2_CR42","DOI":"10.1109\/ICVD.2005.47"},{"doi-asserted-by":"crossref","unstructured":"Devabhaktuni, V.K., Zhang, Q.J.: Neural network training-driven adaptive sampling algorithm. In: Proceedings of 30th European Microwave Conference, Paris, France, vol.\u00a03, pp. 222\u2013225 (2000)","key":"2_CR43","DOI":"10.1109\/EUMA.2000.338591"},{"key":"2_CR44","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1137\/0910022","volume":"10","author":"T.G. Robertazzi","year":"1989","unstructured":"Robertazzi, T.G., Schwartz, S.C.: An accelerated sequential algorithm for producing d-optimal designs. SIAM Journal on scientific Computing\u00a010, 341\u2013358 (1989)","journal-title":"SIAM Journal on scientific Computing"},{"issue":"11","key":"2_CR45","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1016\/S0305-0548(03)00146-1","volume":"31","author":"A.C. Keys","year":"2004","unstructured":"Keys, A.C., Rees, L.P.: A sequential-design metamodeling strategy for simulation optimization. Comput. Oper. Res.\u00a031(11), 1911\u20131932 (2004)","journal-title":"Comput. Oper. Res."},{"unstructured":"Sasena, M.: Flexibility and Efficiency Enhancements For Constrainted Global Design Optimization with Kriging Approximations. PhD thesis, University of Michigan (2002)","key":"2_CR46"},{"doi-asserted-by":"crossref","unstructured":"Jin, R., Chen, W., Sudjianto, A.: On sequential sampling for global metamodeling in engineering design, detc-dac34092. In: ASME Design Automation Conference, Montreal, Canada (2002)","key":"2_CR47","DOI":"10.1115\/DETC2002\/DAC-34092"},{"issue":"4","key":"2_CR48","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1002\/mmce.10032","volume":"12","author":"R. Lehmensiek","year":"2002","unstructured":"Lehmensiek, R., Meyer, P., Muller, M.: Adaptive sampling applied to multivariate, multiple output rational interpolation models with applications to microwave circuits. International Journal of RF and microwave computer aided engineering\u00a012(4), 332\u2013340 (2002)","journal-title":"International Journal of RF and microwave computer aided engineering"},{"doi-asserted-by":"crossref","unstructured":"Deschrijver, D., Dhaene, T.: Rational modeling of spectral data using orthonormal vector fitting. In: Proceedings of 9th IEEE Workshop on Signal Propagation on Interconnects, 2005, pp. 111\u2013114 (2005)","key":"2_CR49","DOI":"10.1109\/SPI.2005.1500915"},{"key":"2_CR50","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.jhydrol.2005.03.013","volume":"314","author":"G. Kingston","year":"2005","unstructured":"Kingston, G., Maier, H., Lambert, M.: Calibration and validation of neural networks to ensure physically plausible hydrological modeling. Journal of Hydrology\u00a0314, 158\u2013176 (2005)","journal-title":"Journal of Hydrology"},{"issue":"3","key":"2_CR51","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.asoc.2005.02.002","volume":"6","author":"S. Srinivasulu","year":"2006","unstructured":"Srinivasulu, S., Jain, A.: A comparative analysis of training methods for artificial neural network rainfall-runoff models. Appl. Soft Comput.\u00a06(3), 295\u2013306 (2006)","journal-title":"Appl. Soft Comput."},{"doi-asserted-by":"crossref","unstructured":"Wolpert, D.: The supervised learning no-free-lunch theorems. In: Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications (2001)","key":"2_CR52","DOI":"10.1007\/978-1-4471-0123-9_3"},{"issue":"4","key":"2_CR53","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1109\/TADVP.2007.901567","volume":"30","author":"P. Triverio","year":"2007","unstructured":"Triverio, P., Grivet-Talocia, S., Nakhla, M., Canavero, F.G., Achar, R.: Stability, causality, and passivity in electrical interconnect models. IEEE Transactions on Advanced Packaging\u00a030(4), 795\u2013808 (2007)","journal-title":"IEEE Transactions on Advanced Packaging"},{"key":"2_CR54","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1162\/neco.2007.08-06-316","volume":"20","author":"M. Ihme","year":"2008","unstructured":"Ihme, M., Marsden, A.L., Pitsch, H.: Generation of optimal artificial neural networks using a pattern search algorithm: Application to approximation of chemical systems. Neural Computation\u00a020, 573\u2013601 (2008)","journal-title":"Neural Computation"},{"unstructured":"Lessmann, S., Stahlbock, R., Crone, S.: Genetic algorithms for support vector machine model selection. In: International Joint Conference on Neural Networks, 2006. IJCNN 2006, pp. 3063\u20133069 (2006)","key":"2_CR55"},{"issue":"3","key":"2_CR56","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1137\/040603954","volume":"28","author":"G.L. JiGuan","year":"2006","unstructured":"JiGuan, G.L.: Modeling test responses by multivariable polynomials of higher degrees. SIAM Journal on Scientific Computing\u00a028(3), 832\u2013867 (2006)","journal-title":"SIAM Journal on Scientific Computing"},{"key":"2_CR57","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0378-3758(00)00105-1","volume":"90","author":"K. Ye","year":"2000","unstructured":"Ye, K., Li, W., Sudjianto, A.: Algorithmic construction of optimal symmetric latin hypercube designs. Journal of Statistical Planning and Inference\u00a090, 145\u2013159 (2000)","journal-title":"Journal of Statistical Planning and Inference"},{"doi-asserted-by":"crossref","unstructured":"Gorissen, D., Dhaene, T., Demeester, P., Broeckhove, J.: Grid enabled surrogate modeling. In: The Encyclopedia of Grid Computing Technologies and Applications (in press) (2008)","key":"2_CR58","DOI":"10.4018\/978-1-60566-184-1.ch025"},{"issue":"2","key":"2_CR59","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K. Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation\u00a06(2), 182\u2013197 (2002)","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"8","key":"2_CR60","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1029\/2002WR001746","volume":"39","author":"J.A. Vrugt","year":"2003","unstructured":"Vrugt, J.A., Gupta, H.V., Bouten, W., Sorooshian, S.: A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research\u00a039(8), 1214\u20131233 (2003)","journal-title":"Water Resources Research"},{"unstructured":"Gorissen, D.: Heterogeneous evolution of surrogate models. Master\u2019s thesis, Master of AI, Katholieke Universiteit Leuven, KUL (2007)","key":"2_CR61"},{"doi-asserted-by":"crossref","unstructured":"Gorissen, D., Tommasi, L.D., 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)","key":"2_CR62","DOI":"10.1109\/CEC.2008.4630917"},{"key":"2_CR63","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1145\/1276958.1277203","volume-title":"GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation","author":"D. Lim","year":"2007","unstructured":"Lim, D., Ong, Y.S., Jin, Y., Sendhoff, B.: A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1288\u20131295. ACM, New York (2007)"},{"key":"2_CR64","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s00158-006-0051-9","volume":"33","author":"T. Goel","year":"2007","unstructured":"Goel, T., Haftka, R., Shyy, W., Queipo, N.: Ensemble of surrogates. Structural and Multidisciplinary Optimization\u00a033, 199\u2013216 (2007)","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"2_CR65","volume-title":"Microwave Engineering","author":"D.M. Pozar","year":"1998","unstructured":"Pozar, D.M.: Microwave Engineering, 2nd edn. John Wiley and Sons, Chichester (1998)","edition":"2"},{"unstructured":"Foresee, F., Hagan, M.: Gauss-newton approximation to bayesian regularization. In: Proceedings of the 1997 International Joint Conference on Neural Networks, pp. 1930\u20131935 (1997)","key":"2_CR66"},{"unstructured":"Wang, Z.: Airfoil geometry design for minimum drag. Technical Report AAE 550, Purdue University (2005)","key":"2_CR67"},{"unstructured":"UIUC Airfoil Coordinates Database (2008), http:\/\/www.ae.uiuc.edu\/m-selig\/ads\/coord_database.html","key":"2_CR68"},{"unstructured":"Design and analysis of subsonic isolated airfoils (2008), http:\/\/web.mit.edu\/drela\/public\/web\/xfoil\/","key":"2_CR69"},{"issue":"4","key":"2_CR70","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/s10898-006-9029-9","volume":"36","author":"D.E. Finkel","year":"2006","unstructured":"Finkel, D.E., Kelley, C.T.: Additive scaling and the direct algorithm. J. of Global Optimization\u00a036(4), 597\u2013608 (2006)","journal-title":"J. of Global Optimization"}],"container-title":["Studies in Computational Intelligence","Foundations of Computational, Intelligence Volume 1"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-01082-8_2.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T21:40:29Z","timestamp":1633383629000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-01082-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009]]},"ISBN":["9783642010811","9783642010828"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-01082-8_2","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2009]]}}}