10 Sequential Parameter Optimization for Mixed-Discrete Problems
353
3. M. Asadi, J. Goldak, Combinatorial optimization of weld sequence by using a surrogate model
to mitigate a weld distortion. Int. J. Mech. Mater. Des. 7(2), 123–139 (2011)
4. L. Bajer, M. Holeˇ na, Surrogate model for continuous and discrete genetic optimization based
on RBF networks, in Intelligent Data Engineering and Automated Learning – IDEAL 2010.
Lecture Notes in Computer Science, vol. 6283 (2010), pp. 251–258
5. J.W. Bandler, R.M. Biernacki, S.H. Chen, P.A. Grobelny, R.H. Hemmers, Space mapping
technique for electromagnetic optimization. IEEE Trans. Microw. Theory Tech. 42(12), 2536–
2544 (1994)
6. T. Bartz-Beielstein, Experimental Research in Evolutionary Computation—The New Experimentalism. Natural Computing Series (Springer, Berlin, Heidelberg, New York, 2006)
7. T. Bartz-Beielstein, S. Markon, Tuning search algorithms for real-world applications: a
regression tree based approach, in Proceedings 2004 Congress on Evolutionary Computation
(CEC’04), Portland, OR, ed. by G.W. Greenwood (IEEE, Piscataway NJ, 2004), pp. 1111–1118
8. T. Bartz-Beielstein, M. Zaefferer, Model-based methods for continuous and discrete global
optimization. Appl. Soft Comput. 55, 154–167 (2017)
9. T. Bartz-Beielstein, L. Gentile, M. Zaefferer, In a nutshell: sequential parameter optimization.
Technical report, TH Köln, 2017
10. A. Bergman, M.W. Feldman, Recombination dynamics and the fitness landscape. Phys. D:
Nonlinear Phenom. 56(1), 57–67 (1992)
11. B. Borchers, J.E. Mitchell, An improved branch and bound algorithm for mixed integer
nonlinear programs. Comput. Oper. Res. 21(4), 359–367 (1994)
12. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)
13. C. Ding, X. He, H. Zha, H.D. Simon, Adaptive dimension reduction for clustering high
dimensional data, in 2002 IEEE International Conference on Data Mining, 2002. ICDM 2003.
Proceedings (IEEE, New York, 2002), pp. 147–154
14. N.R. Draper, H. Smith, Applied Regression Analysis, vol. 326 (Wiley, New York, 2014)
15. M.A. Duran, I.E. Grossmann, An outer-approximation algorithm for a class of mixed-integer
nonlinear programs. Math. Program. 36(3), 307–339 (1986
16. R. Filomeno Coelho, M. Herrera, M. Xiao, W. Zhang, On-line metamodel-assisted optimization with mixed variables, in Evolutionary Algorithms and Metaheuristics in Civil Engineering
and Construction Management. Computational Methods in Applied Sciences, vol. 39, ed. by
J. Magalhães-Mendes, D. Greiner (Springer International Publishing, Basel, 2015), pp. 1–15
17. C.A. Floudas, Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications
(Oxford University Press, Oxford, 1995)
18. L. Fonseca, H. Barbosa, A. Lemonge, A similarity-based surrogate model for expensive
evolutionary optimization with fixed budget of simulations, in Proceedings of the Congress
on Evolutionary Computation (CEC’09) (IEEE, New York, NY, 2009), pp. 867–874
19. A. Forrester, A. Keane et al., Engineering Design via Surrogate Modelling: A Practical Guide
(Wiley, Chichester, 2008)
20. A.M. Geoffrion, Generalized benders decomposition. J. Optim. Theory Appl. 10(4), 237–260
(1972)
21. T. Hemker, Derivative Free Surrogate Optimization for Mixed-Integer Nonlinear Black Box
Problems in Engineering. PhD thesis, Technische Universität Darmstadt, December 2008
22. M. Herrera, A. Guglielmetti, M. Xiao, R. Filomeno Coelho, Metamodel-assisted optimization
based on multiple kernel regression for mixed variables. Struct. Multidiscipl. Optim. 49(6),
979–991 (2014)
23. K. Hibbitt, Abaqus: User’s Manual: Version 6.13: Hibbitt (Karlsson & Sorensen, Incorporated,
Providence, RI, 2013)
24. R.R. Hocking, A biometrics invited paper. the analysis and selection of variables in linear
regression. Biometrics 32(1), 1–49 (1976)
25. F. Hutter, H.H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general
algorithm configuration (extended version). Technical Report TR-2010-10, University of
British Columbia, Department of Computer Science, 2010. Available online http://www.cs.
ubc.ca/~hutter/papers/10-TR-SMAC.pdf
353
3. M. Asadi, J. Goldak, Combinatorial optimization of weld sequence by using a surrogate model
to mitigate a weld distortion. Int. J. Mech. Mater. Des. 7(2), 123–139 (2011)
4. L. Bajer, M. Holeˇ na, Surrogate model for continuous and discrete genetic optimization based
on RBF networks, in Intelligent Data Engineering and Automated Learning – IDEAL 2010.
Lecture Notes in Computer Science, vol. 6283 (2010), pp. 251–258
5. J.W. Bandler, R.M. Biernacki, S.H. Chen, P.A. Grobelny, R.H. Hemmers, Space mapping
technique for electromagnetic optimization. IEEE Trans. Microw. Theory Tech. 42(12), 2536–
2544 (1994)
6. T. Bartz-Beielstein, Experimental Research in Evolutionary Computation—The New Experimentalism. Natural Computing Series (Springer, Berlin, Heidelberg, New York, 2006)
7. T. Bartz-Beielstein, S. Markon, Tuning search algorithms for real-world applications: a
regression tree based approach, in Proceedings 2004 Congress on Evolutionary Computation
(CEC’04), Portland, OR, ed. by G.W. Greenwood (IEEE, Piscataway NJ, 2004), pp. 1111–1118
8. T. Bartz-Beielstein, M. Zaefferer, Model-based methods for continuous and discrete global
optimization. Appl. Soft Comput. 55, 154–167 (2017)
9. T. Bartz-Beielstein, L. Gentile, M. Zaefferer, In a nutshell: sequential parameter optimization.
Technical report, TH Köln, 2017
10. A. Bergman, M.W. Feldman, Recombination dynamics and the fitness landscape. Phys. D:
Nonlinear Phenom. 56(1), 57–67 (1992)
11. B. Borchers, J.E. Mitchell, An improved branch and bound algorithm for mixed integer
nonlinear programs. Comput. Oper. Res. 21(4), 359–367 (1994)
12. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)
13. C. Ding, X. He, H. Zha, H.D. Simon, Adaptive dimension reduction for clustering high
dimensional data, in 2002 IEEE International Conference on Data Mining, 2002. ICDM 2003.
Proceedings (IEEE, New York, 2002), pp. 147–154
14. N.R. Draper, H. Smith, Applied Regression Analysis, vol. 326 (Wiley, New York, 2014)
15. M.A. Duran, I.E. Grossmann, An outer-approximation algorithm for a class of mixed-integer
nonlinear programs. Math. Program. 36(3), 307–339 (1986
16. R. Filomeno Coelho, M. Herrera, M. Xiao, W. Zhang, On-line metamodel-assisted optimization with mixed variables, in Evolutionary Algorithms and Metaheuristics in Civil Engineering
and Construction Management. Computational Methods in Applied Sciences, vol. 39, ed. by
J. Magalhães-Mendes, D. Greiner (Springer International Publishing, Basel, 2015), pp. 1–15
17. C.A. Floudas, Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications
(Oxford University Press, Oxford, 1995)
18. L. Fonseca, H. Barbosa, A. Lemonge, A similarity-based surrogate model for expensive
evolutionary optimization with fixed budget of simulations, in Proceedings of the Congress
on Evolutionary Computation (CEC’09) (IEEE, New York, NY, 2009), pp. 867–874
19. A. Forrester, A. Keane et al., Engineering Design via Surrogate Modelling: A Practical Guide
(Wiley, Chichester, 2008)
20. A.M. Geoffrion, Generalized benders decomposition. J. Optim. Theory Appl. 10(4), 237–260
(1972)
21. T. Hemker, Derivative Free Surrogate Optimization for Mixed-Integer Nonlinear Black Box
Problems in Engineering. PhD thesis, Technische Universität Darmstadt, December 2008
22. M. Herrera, A. Guglielmetti, M. Xiao, R. Filomeno Coelho, Metamodel-assisted optimization
based on multiple kernel regression for mixed variables. Struct. Multidiscipl. Optim. 49(6),
979–991 (2014)
23. K. Hibbitt, Abaqus: User’s Manual: Version 6.13: Hibbitt (Karlsson & Sorensen, Incorporated,
Providence, RI, 2013)
24. R.R. Hocking, A biometrics invited paper. the analysis and selection of variables in linear
regression. Biometrics 32(1), 1–49 (1976)
25. F. Hutter, H.H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general
algorithm configuration (extended version). Technical Report TR-2010-10, University of
British Columbia, Department of Computer Science, 2010. Available online http://www.cs.
ubc.ca/~hutter/papers/10-TR-SMAC.pdf