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Evol. Comput. 9(3), 303–317 (2005)
27. R. Jin, W. Chen, T.W. Simpson, Comparative studies of metamodelling techniques under
multiple modelling criteria. Struct. Multidiscipl. Optim. 23(1), 1–13 (2001)
28. D.R. Jones, M. Schonlau, W.J. Welch, Efficient global optimization of expensive Black-Box
functions. J. Global Optim. 13, 455–492 (1998)
29. T. Kohonen, The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)
30. R. Li, M.T.M. Emmerich, J. Eggermont, E.G.P. Bovenkamp, T. Bäck, J. Dijkstra, J.H.C. Reiber,
Metamodel-assisted mixed integer evolution strategies and their application to intravascular
ultrasound image analysis, in 2008 IEEE Congress on Evolutionary Computation (CEC)
(IEEE, New York, 2008), pp. 2764–2771
31. R. Li, M.T. Emmerich, J. Eggermont, T. Bäck, M. Schütz, J. Dijkstra, J.H. Reiber, Mixed
integer evolution strategies for parameter optimization. Evol. Comput. 21(1), 29–64 (2013)
32. D. Liu, A. Litvinenko, C. Schillings, V. Schulz. Quantification of airfoil geometry-induced
aerodynamic uncertainties—comparison of approaches. SIAM/ASA J. Uncertain. Quant. 5(1),
334–352 (2017)
33. R. Lohmann, Structure evolution and incomplete induction. Biol. Cybern. 69(4), 319–326
(1993)
34. M.D. McKay, R.J. Beckman, W.J. Conover, A comparison of three methods for selecting
values of input variables in the analysis of output from a computer code. Technometrics 21(2),
239–245 (1979)
35. J. Moˇ ckus, On Bayesian methods for seeking the extremum, in Optimization Techniques IFIP
Technical Conference (1974), pp. 400–404
36. D.C. Montgomery, Design and Analysis of Experiments (Wiley, New York, 2017)
37. A. Moraglio, A. Kattan, Geometric generalisation of surrogate model based optimisation
to combinatorial spaces, in Proceedings of the 11th European Conference on Evolutionary
Computation in Combinatorial Optimization, EvoCOP’11 (Springer, Berlin, Heidelberg,
2011), pp. 142–154
38. M.D. Morris, Factorial sampling plans for preliminary computational experiments. Technometrics 33(2), 161–174 (1991)
39. P. Composites Ltd. Mechanical properties of carbon fibre composite materials. http://www.
performance-composites.com/carbonfibre/mechanicalproperties_2.asp. Date: 2018-07-01
40. G. Papa, Parameter-less algorithm for evolutionary-based optimization. Comput. Optim. Appl.
56(1), 209–229 (2013)
41. P.A. Romero, A. Krause, F.H. Arnold, Navigating the protein fitness landscape with Gaussian
processes. Proc. Natl. Acad. Sci. 110(3), E193–E201 (2013)
42. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error
propagation. Technical report, California Univ San Diego La Jolla Inst for Cognitive Science,
1985
43. A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto, Sensitivity Analysis in Practice (Wiley,
New York, 2004)
44. M. Schütz, J. Sprave, Application of parallel mixed-integer evolution strategies with mutation
rate pooling, in Proceedings of the Fifth Annual Conference on Evolutionary Programming
(1996). Citeseer
45. H.-P. Schwefel, Evolution and optimum seeking. sixth-generation computer technology series
(1995)
46. S. Shan, G.G. Wang, Survey of modeling and optimization strategies to solve high-dimensional
design problems with computationally-expensive black-box functions. Struct. Multidiscipl.
Optim. 41(2), 219–241 (2010)
47. J. Smith, C. Stone, M. Serpell, Exploiting diverse distance metrics for surrogate-based
optimisation of ordering problems: a case study, in Proceedings of the 2016 on Genetic and
Evolutionary Computation Conference, GECCO ’16, pp. 701–708 (ACM, New York, NY,
2016)