382
M. Antoniou et al.
29. H. Muhlenbein, How genetic algorithms really work: I. Mutation and hillclimbing, in
Proceedings of the 2nd International Conference on Parallel Problem Solving from Nature,
1992, 1992 [Online]. Available: https://ci.nii.ac.jp/naid/10022158367/en/
30. H.-G. Beyer, B. Sendhoff, Covariance matrix adaptation revisited – the cmsa evolution strategy
–, in Parallel Problem Solving from Nature – PPSN X, G. Rudolph, T. Jansen, N. Beume,
S. Lucas, C. Poloni (Springer, Berlin, Heidelberg, 2008), pp. 123–132
31. M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle, M. Birattari, The irace
package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58
(2016)
32. T. Bartz-Beielstein, O. Flasch, P. Koch, W. Konen, SPOT: a toolbox for interactive and automatic tuning in the R environment, in Proceedings 20. Workshop Computational Intelligence,
ed. by F. Hoffmann, E. Hüllermeier (Universitätsverlag Karlsruhe, Karlsruhe 2010), pp. 264–
273
33. C. Ansótegui, Y. Malitsky, H. Samulowitz, M. Sellmann, K. Tierney, Model-based genetic
algorithms for algorithm configuration, in Proceedings of the 24th International Conference on
Artificial Intelligence, IJCAI’15 (AAAI Press, Menlo Park, CA, 2015), pp. 733–739 [Online].
Available http://dl.acm.org/citation.cfm?id=2832249.2832351
34. F. Hutter, H.H. Hoos, K. Leyton-Brown, T. Stützle, Paramils: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36(1), 267–306 (2009) [Online]. Available http://dl.acm.
org/citation.cfm?id=1734953.1734959
35. F. Hutter, H.H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general
algorithm configuration, in Proceedings of the 5th International Conference on Learning and
Intelligent Optimization, LION’05 (Springer, Berlin, Heidelberg, 2011), pp. 507–523 [Online].
Available http://dx.doi.org/10.1007/978-3-642-25566-3_40
36. J. Snoek, H. Larochelle, R.P. Adams, Practical Bayesian optimization of machine learning
algorithms, in Advances in Neural Information Processing Systems (2012), pp. 2951–2959
37. A.E. Eiben, J. Smith, From evolutionary computation to the evolution of things. Nature
521(7553), 476–482 (2015) [Online]. Available https://www.nature.com/articles/nature14544
38. J. Hesser, R. Männer, Towards an optimal mutation probability for genetic algorithms, in
Proceedings of the 1st Workshop on Parallel Problem Solving from Nature, PPSN I (Springer,
Berlin, Heidelberg, 1991), pp. 23–32 [Online]. Available http://dl.acm.org/citation.cfm?id=
645821.670199
39. J.C. Costa, R. Tavares, A. Rosa, An experimental study on dynamic random variation
of population size, in IEEE SMC’99 Conference Proceedings. 1999 IEEE International
Conference on Systems, Man, and Cybernetics, October 1999, vol. 1, pp. 607–612
40. T. Jansen, I. Wegener, On the analysis of a dynamic evolutionary algorithm. J. Discrete
Algorith. 4(1), 181–199 (2006) [Online]. Available http://www.sciencedirect.com/science/
article/pii/S1570866705000109
41. V.K. Koumousis, C.P. Katsaras, A saw-tooth genetic algorithm combining the effects of
variable population size and reinitialization to enhance performance. IEEE Trans. Evol.
Comput. 10(1), 19–28 (2006)
42. T. Hu, S. Harding, W. Banzhaf, Variable population size and evolution acceleration: a case
study with a parallel evolutionary algorithm. Genet. Program. Evol. Mach. 11(2), 205–225
(2010) [Online]. Available http://dx.doi.org/10.1007/s10710-010-9105-2
43. B. Doerr, C. Doerr, Optimal static and self-adjusting parameter choices for the (1+(λ,λ))
genetic algorithm. Algorithmica 80(5), 1658–1709 (2018) [Online]. Available https://doi.org/
10.1007/s00453-017-0354-9
44. R. Tanabe, A. Fukunaga, Success-history based parameter adaptation for differential evolution,
in IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, 2013), pp. 71–78
45. P. Merz, Advanced fitness landscape analysis and the performance of memetic algorithms.
Evol. Comput. 12(3), 303–325 (2004)
46. J. Zhang, Z.-H. Zhan, Y. Lin, N. Chen, Y.-J. Gong, J.-H. Zhong, H.S. Chung, Y. Li, Y.-H. Shi,
Evolutionary computation meets machine learning: a survey. IEEE Comput. Intell. Mag. 6(4),
68–75 (2011)
M. Antoniou et al.
29. H. Muhlenbein, How genetic algorithms really work: I. Mutation and hillclimbing, in
Proceedings of the 2nd International Conference on Parallel Problem Solving from Nature,
1992, 1992 [Online]. Available: https://ci.nii.ac.jp/naid/10022158367/en/
30. H.-G. Beyer, B. Sendhoff, Covariance matrix adaptation revisited – the cmsa evolution strategy
–, in Parallel Problem Solving from Nature – PPSN X, G. Rudolph, T. Jansen, N. Beume,
S. Lucas, C. Poloni (Springer, Berlin, Heidelberg, 2008), pp. 123–132
31. M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle, M. Birattari, The irace
package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58
(2016)
32. T. Bartz-Beielstein, O. Flasch, P. Koch, W. Konen, SPOT: a toolbox for interactive and automatic tuning in the R environment, in Proceedings 20. Workshop Computational Intelligence,
ed. by F. Hoffmann, E. Hüllermeier (Universitätsverlag Karlsruhe, Karlsruhe 2010), pp. 264–
273
33. C. Ansótegui, Y. Malitsky, H. Samulowitz, M. Sellmann, K. Tierney, Model-based genetic
algorithms for algorithm configuration, in Proceedings of the 24th International Conference on
Artificial Intelligence, IJCAI’15 (AAAI Press, Menlo Park, CA, 2015), pp. 733–739 [Online].
Available http://dl.acm.org/citation.cfm?id=2832249.2832351
34. F. Hutter, H.H. Hoos, K. Leyton-Brown, T. Stützle, Paramils: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36(1), 267–306 (2009) [Online]. Available http://dl.acm.
org/citation.cfm?id=1734953.1734959
35. F. Hutter, H.H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general
algorithm configuration, in Proceedings of the 5th International Conference on Learning and
Intelligent Optimization, LION’05 (Springer, Berlin, Heidelberg, 2011), pp. 507–523 [Online].
Available http://dx.doi.org/10.1007/978-3-642-25566-3_40
36. J. Snoek, H. Larochelle, R.P. Adams, Practical Bayesian optimization of machine learning
algorithms, in Advances in Neural Information Processing Systems (2012), pp. 2951–2959
37. A.E. Eiben, J. Smith, From evolutionary computation to the evolution of things. Nature
521(7553), 476–482 (2015) [Online]. Available https://www.nature.com/articles/nature14544
38. J. Hesser, R. Männer, Towards an optimal mutation probability for genetic algorithms, in
Proceedings of the 1st Workshop on Parallel Problem Solving from Nature, PPSN I (Springer,
Berlin, Heidelberg, 1991), pp. 23–32 [Online]. Available http://dl.acm.org/citation.cfm?id=
645821.670199
39. J.C. Costa, R. Tavares, A. Rosa, An experimental study on dynamic random variation
of population size, in IEEE SMC’99 Conference Proceedings. 1999 IEEE International
Conference on Systems, Man, and Cybernetics, October 1999, vol. 1, pp. 607–612
40. T. Jansen, I. Wegener, On the analysis of a dynamic evolutionary algorithm. J. Discrete
Algorith. 4(1), 181–199 (2006) [Online]. Available http://www.sciencedirect.com/science/
article/pii/S1570866705000109
41. V.K. Koumousis, C.P. Katsaras, A saw-tooth genetic algorithm combining the effects of
variable population size and reinitialization to enhance performance. IEEE Trans. Evol.
Comput. 10(1), 19–28 (2006)
42. T. Hu, S. Harding, W. Banzhaf, Variable population size and evolution acceleration: a case
study with a parallel evolutionary algorithm. Genet. Program. Evol. Mach. 11(2), 205–225
(2010) [Online]. Available http://dx.doi.org/10.1007/s10710-010-9105-2
43. B. Doerr, C. Doerr, Optimal static and self-adjusting parameter choices for the (1+(λ,λ))
genetic algorithm. Algorithmica 80(5), 1658–1709 (2018) [Online]. Available https://doi.org/
10.1007/s00453-017-0354-9
44. R. Tanabe, A. Fukunaga, Success-history based parameter adaptation for differential evolution,
in IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, 2013), pp. 71–78
45. P. Merz, Advanced fitness landscape analysis and the performance of memetic algorithms.
Evol. Comput. 12(3), 303–325 (2004)
46. J. Zhang, Z.-H. Zhan, Y. Lin, N. Chen, Y.-J. Gong, J.-H. Zhong, H.S. Chung, Y. Li, Y.-H. Shi,
Evolutionary computation meets machine learning: a survey. IEEE Comput. Intell. Mag. 6(4),
68–75 (2011)