17 Introduction to Evidence-Based Robust Optimisation
573
76. Y. Jin, A comprehensive survey of fitness approximation in evolutionary computation. Soft
Comput. 9(1), 3–12 (2005)
77. J. Marzat, E. Walker, H. Piet-Lahanier, Worst-case global optimization of black-box functions
through kriging and relaxation. J. Global Optim. 55, 707–727 (2013)
78. G. Filippi, M. Vasile, A memetic approach to the solution of constrained min-max problems,
in 2019 IEEE Congress on Evolutionary Computation (CEC) (2019), pp. 506–513. https://
doi.org/10.1109/CEC.2019.8790124
79. J.C. Helton, J. Johnson, W.L. Oberkampf, C. Sallaberry, Sensitivity analysis in conjunction
with evidence theory representations of epistemic uncertainty. Reliab. Eng. Syst. Saf. 91(10–
11), 1414–1434 (2006)
80. C. Joslyn, J.C. Helton, Bounds on belief and plausibility of functionality propagated random
sets, in 2002 Annual Meetings of the North American Fuzzy Information Processing Society,
Proceedings, ed. by J. Keller, O. Nasraoui, June 2002, New Orleans, vol. 2729 (IEEE,
Piscataway, 2002), pp. 412–417
81. C. Joslyn, V. Kreinovich, Convergence properties of an interval probabilistic approach to
system reliability estimation. Int. J. Gen. Syst. 34(4), 465–482 (2005)
82. M. Di Carlo, M. Vasile, C. Greco, R. Epenoy, Robust optimisation of low-thrust interplanetary
transfers using evidence theory, in 29th AAS/AIAA Space Flight Mechanics Meeting (2019)
83. F. Voorbraak, A computationally efficient approximation of Dempster-Shafer theory. Int. J.
Man-Mach. Stud. 30, 525–536 (1989)
84. D. Dubois, H. Prade, Consonant approximations of belief functions. Int. J. Approx. Reason.
4, 419–449 (1990)
85. B. Tessem, Approximations for efficient computation in the theory of evidence. Artif. Intell.
61(2), 315–329 (1993)
86. M. Bauer, Approximations for decision making in the Dempster-Shafer theory of evidence,
in Proceedings of Twelfth International Conference on Uncertainty in Artificial Intelligence
(1996), pp. 73–80
87. D. Han, J. Dezert, Y. Yang, Two novel methods for BBA approximation based on focal
element redundancy, in 18th International Conference on Information Fusion (2015), p. 428–
434
88. T. Denux, Inner and outer approximation of belief structures using a hierarchical clustering
approach. Int. J. Uncertainty Fuzziness Knowledge Based Syst. 9(4), 437–460 (2001)
89. D. Harmanec, Faithful approximations of belief functions, in Proceedings of 15th Conference
on Uncertainty in Artificial Intelligence (1999), p. 2718
90. M. Vasile, G. Filippi, C. Ortega, A. Riccardi, Fast belief estimation in evidence network
models, in EUROGEN 2017 (2017)
91. S. Alicino, M. Vasile, Evidence-based preliminary design of spacecraft, in 6th International
Conference on Systems & Concurrent Engineering for Space Applications SECESA 2014
(2014)
92. G. Filippi, M. Vasile, D. Krpelik, P.Z. Korondi, M. Marchi, C. Poloni, Space systems
resilience optimisation under epistemic uncertainty. Acta Astronaut. 165, 195–210 (2019).
https://doi.org/10.1016/j.actaastro.2019.08.024
573
76. Y. Jin, A comprehensive survey of fitness approximation in evolutionary computation. Soft
Comput. 9(1), 3–12 (2005)
77. J. Marzat, E. Walker, H. Piet-Lahanier, Worst-case global optimization of black-box functions
through kriging and relaxation. J. Global Optim. 55, 707–727 (2013)
78. G. Filippi, M. Vasile, A memetic approach to the solution of constrained min-max problems,
in 2019 IEEE Congress on Evolutionary Computation (CEC) (2019), pp. 506–513. https://
doi.org/10.1109/CEC.2019.8790124
79. J.C. Helton, J. Johnson, W.L. Oberkampf, C. Sallaberry, Sensitivity analysis in conjunction
with evidence theory representations of epistemic uncertainty. Reliab. Eng. Syst. Saf. 91(10–
11), 1414–1434 (2006)
80. C. Joslyn, J.C. Helton, Bounds on belief and plausibility of functionality propagated random
sets, in 2002 Annual Meetings of the North American Fuzzy Information Processing Society,
Proceedings, ed. by J. Keller, O. Nasraoui, June 2002, New Orleans, vol. 2729 (IEEE,
Piscataway, 2002), pp. 412–417
81. C. Joslyn, V. Kreinovich, Convergence properties of an interval probabilistic approach to
system reliability estimation. Int. J. Gen. Syst. 34(4), 465–482 (2005)
82. M. Di Carlo, M. Vasile, C. Greco, R. Epenoy, Robust optimisation of low-thrust interplanetary
transfers using evidence theory, in 29th AAS/AIAA Space Flight Mechanics Meeting (2019)
83. F. Voorbraak, A computationally efficient approximation of Dempster-Shafer theory. Int. J.
Man-Mach. Stud. 30, 525–536 (1989)
84. D. Dubois, H. Prade, Consonant approximations of belief functions. Int. J. Approx. Reason.
4, 419–449 (1990)
85. B. Tessem, Approximations for efficient computation in the theory of evidence. Artif. Intell.
61(2), 315–329 (1993)
86. M. Bauer, Approximations for decision making in the Dempster-Shafer theory of evidence,
in Proceedings of Twelfth International Conference on Uncertainty in Artificial Intelligence
(1996), pp. 73–80
87. D. Han, J. Dezert, Y. Yang, Two novel methods for BBA approximation based on focal
element redundancy, in 18th International Conference on Information Fusion (2015), p. 428–
434
88. T. Denux, Inner and outer approximation of belief structures using a hierarchical clustering
approach. Int. J. Uncertainty Fuzziness Knowledge Based Syst. 9(4), 437–460 (2001)
89. D. Harmanec, Faithful approximations of belief functions, in Proceedings of 15th Conference
on Uncertainty in Artificial Intelligence (1999), p. 2718
90. M. Vasile, G. Filippi, C. Ortega, A. Riccardi, Fast belief estimation in evidence network
models, in EUROGEN 2017 (2017)
91. S. Alicino, M. Vasile, Evidence-based preliminary design of spacecraft, in 6th International
Conference on Systems & Concurrent Engineering for Space Applications SECESA 2014
(2014)
92. G. Filippi, M. Vasile, D. Krpelik, P.Z. Korondi, M. Marchi, C. Poloni, Space systems
resilience optimisation under epistemic uncertainty. Acta Astronaut. 165, 195–210 (2019).
https://doi.org/10.1016/j.actaastro.2019.08.024
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