11 Parameter Control in Evolutionary Optimisation
381
11. A. Aleti, I. Moser, A systematic literature review of adaptive parameter control methods for
evolutionary algorithms. ACM Comput. Surv. 49(3), 56:1–56:35 (2016) [Online]. Available
http://doi.acm.org/10.1145/2996355
12. G. Papa, Parameter-less algorithm for evolutionary-based optimization. Comput. Optim. Appl.
56(1), 209–229 (2013) [Online]. Available https://link.springer.com/article/10.1007/s10589013-9565-4
13. C. Doerr, Dynamic parameter choices in evolutionary computation, in Proceedings of
the Genetic and Evolutionary Computation Conference Companion, GECCO ’18 (ACM,
New York, NY, 2018), pp. 800–830 [Online]. Available http://doi.acm.org/10.1145/3205651.
3207851
14. A.E. Eiben, S.K. Smit, Evolutionary Algorithm Parameters and Methods to Tune Them
(Springer, Berlin, Heidelberg, 2012), pp. 15–36 [Online]. Available https://doi.org/10.1007/
978-3-642-21434-9_2
15. P. Machado, A. Leitão, Evolving fitness functions for mating selection, in Genetic Programming, ed. by S. Silva, J.A. Foster, M. Nicolau, P. Machado, M. Giacobini (Springer, Berlin,
Heidelberg, 2011), pp. 227–238
16. C.M. Fonseca, M.B. Correia, Developing redundant binary representations for genetic search,
in 2005 IEEE Congress on Evolutionary Computation, September 2005, vol. 2, pp. 1675–1682
17. E.K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Özcan, J.R. Woodward, A Classification
of Hyper-heuristic Approaches (Springer US, Boston, MA, 2010), pp. 449–468 [Online].
Available https://doi.org/10.1007/978-1-4419-1665-5_15
18. G. Papa, J. Šilc, Evolutionary synthesis algorithm - genetic operators tuning, in Advances in
Intelligent Systems, Fuzzy Systems, Evolutionary Computation (WSEAS Press, Athens, 2002),
pp. 256–261
19. A.E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms. IEEE
Trans. Evol. Comput. 3(2), 124–141 (1999)
20. I. Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der
biologischen Evolution (Frommann-Holzboog, Stuttgart, 1973)
21. D.E. Goldberg, Messy genetic algorithms revisited: studies in mixed size and scale. Complex
Syst. 4(4), 15–444 (1990)
22. T.-L. Yu, D.E. Goldberg, Toward an understanding of the quality and efficiency of model
building for genetic algorithms, in Genetic and Evolutionary Computation—GECCO 2004,
vol. 3103, ed. by T. Kanade, J. Kittler, J.M. Kleinberg, F. Mattern, J.C. Mitchell, M. Naor,
O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M.Y. Vardi,
G. Weikum, K. Deb (Springer, Berlin, Heidelberg, 2004), pp. 367–378
23. T. Bäck, Optimal mutation rates in genetic search, in Proceedings of the fifth International
Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, CA, 1993), pp. 2–8
24. C. Doerr, M. Wagner, Simple on-the-fly parameter selection mechanisms for two classical
discrete black-box optimization benchmark problems, in Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2018, Kyoto, July 15–19, 2018, pp. 943–950
[Online]. Available https://doi.org/10.1145/3205455.3205560
25. N. Hansen, A. Ostermeier, Completely derandomized self-adaptation in evolution strategies.
Evol. Comput. 9(2), 159–195, June 2001 [Online]. Available: http://dx.doi.org/10.1162/
106365601750190398
26. J. Brest, S. Greiner, B. Boškovi´ c, M. Mernik, V. Žumer, Self-adapting control parameters in
differential evolution: a comparative study on numerical benchmark problems. IEEE Trans.
Evol. Comput. 10(6), 646–657 (2006)
27. K.A. De Jong, An analysis of the behavior of a class of genetic adaptive systems, Ph.D.
dissertation, University of Michigan, Ann Arbor, MI, 1975
28. J.J. Grefenstette, Optimization of control parameters for genetic algorithms. IEEE Trans. Syst.
Man Cybern. 16(1), 122–128 (1986)
381
11. A. Aleti, I. Moser, A systematic literature review of adaptive parameter control methods for
evolutionary algorithms. ACM Comput. Surv. 49(3), 56:1–56:35 (2016) [Online]. Available
http://doi.acm.org/10.1145/2996355
12. G. Papa, Parameter-less algorithm for evolutionary-based optimization. Comput. Optim. Appl.
56(1), 209–229 (2013) [Online]. Available https://link.springer.com/article/10.1007/s10589013-9565-4
13. C. Doerr, Dynamic parameter choices in evolutionary computation, in Proceedings of
the Genetic and Evolutionary Computation Conference Companion, GECCO ’18 (ACM,
New York, NY, 2018), pp. 800–830 [Online]. Available http://doi.acm.org/10.1145/3205651.
3207851
14. A.E. Eiben, S.K. Smit, Evolutionary Algorithm Parameters and Methods to Tune Them
(Springer, Berlin, Heidelberg, 2012), pp. 15–36 [Online]. Available https://doi.org/10.1007/
978-3-642-21434-9_2
15. P. Machado, A. Leitão, Evolving fitness functions for mating selection, in Genetic Programming, ed. by S. Silva, J.A. Foster, M. Nicolau, P. Machado, M. Giacobini (Springer, Berlin,
Heidelberg, 2011), pp. 227–238
16. C.M. Fonseca, M.B. Correia, Developing redundant binary representations for genetic search,
in 2005 IEEE Congress on Evolutionary Computation, September 2005, vol. 2, pp. 1675–1682
17. E.K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Özcan, J.R. Woodward, A Classification
of Hyper-heuristic Approaches (Springer US, Boston, MA, 2010), pp. 449–468 [Online].
Available https://doi.org/10.1007/978-1-4419-1665-5_15
18. G. Papa, J. Šilc, Evolutionary synthesis algorithm - genetic operators tuning, in Advances in
Intelligent Systems, Fuzzy Systems, Evolutionary Computation (WSEAS Press, Athens, 2002),
pp. 256–261
19. A.E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms. IEEE
Trans. Evol. Comput. 3(2), 124–141 (1999)
20. I. Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der
biologischen Evolution (Frommann-Holzboog, Stuttgart, 1973)
21. D.E. Goldberg, Messy genetic algorithms revisited: studies in mixed size and scale. Complex
Syst. 4(4), 15–444 (1990)
22. T.-L. Yu, D.E. Goldberg, Toward an understanding of the quality and efficiency of model
building for genetic algorithms, in Genetic and Evolutionary Computation—GECCO 2004,
vol. 3103, ed. by T. Kanade, J. Kittler, J.M. Kleinberg, F. Mattern, J.C. Mitchell, M. Naor,
O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M.Y. Vardi,
G. Weikum, K. Deb (Springer, Berlin, Heidelberg, 2004), pp. 367–378
23. T. Bäck, Optimal mutation rates in genetic search, in Proceedings of the fifth International
Conference on Genetic Algorithms (Morgan Kaufmann, San Mateo, CA, 1993), pp. 2–8
24. C. Doerr, M. Wagner, Simple on-the-fly parameter selection mechanisms for two classical
discrete black-box optimization benchmark problems, in Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2018, Kyoto, July 15–19, 2018, pp. 943–950
[Online]. Available https://doi.org/10.1145/3205455.3205560
25. N. Hansen, A. Ostermeier, Completely derandomized self-adaptation in evolution strategies.
Evol. Comput. 9(2), 159–195, June 2001 [Online]. Available: http://dx.doi.org/10.1162/
106365601750190398
26. J. Brest, S. Greiner, B. Boškovi´ c, M. Mernik, V. Žumer, Self-adapting control parameters in
differential evolution: a comparative study on numerical benchmark problems. IEEE Trans.
Evol. Comput. 10(6), 646–657 (2006)
27. K.A. De Jong, An analysis of the behavior of a class of genetic adaptive systems, Ph.D.
dissertation, University of Michigan, Ann Arbor, MI, 1975
28. J.J. Grefenstette, Optimization of control parameters for genetic algorithms. IEEE Trans. Syst.
Man Cybern. 16(1), 122–128 (1986)