11 Parameter Control in Evolutionary Optimisation
383
47. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller,
Playing atari with deep reinforcement learning (2013). Preprint. arXiv:1312.5602
48. H. Zhang, J. Lu, Adaptive evolutionary programming based on reinforcement learning. Inf.
Sci. 178(4), 971–984 (2008)
49. X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster. IEEE Trans. Evol. Comput.
3(2), 82–102 (1999)
50. O. Kramer, Evolutionary self-adaptation: a survey of operators and strategy parameters. Evol.
Intell. 3(2), 51–65 (2010)
51. T. Bäck, The interaction of mutation rate, selection, and self-adaptation within a genetic
algorithm, in Proceedings of the 2nd Conference on Parallel Problem Solving from Nature,
PPSN 1992, ed. by R. Männer, B. Manderick (North-Holland, Amsterdam, 1992)
52. J. Brest, M.S. Mauˇ cec, Self-adaptive differential evolution algorithm using population size
reduction and three strategies. Soft Comput. 15(11), 2157–2174 (2011) [Online]. Available
https://doi.org/10.1007/s00500-010-0644-5
53. B. Doerr, C. Witt, J. Yang, Runtime analysis for self-adaptive mutation rates, in Proceedings
of the Genetic and Evolutionary Computation Conference, ECCO ’18 (ACM, New York, NY,
2018), pp. 1475–1482 [Online]. Available http://doi.acm.org/10.1145/3205455.3205569
54. P. Siarry, Z. Michalewicz, Advances in Metaheuristics for Hard Optimization. Natural Computing Series, 1st edn. (Springer, New York, 2007)
55. P. Korošec, U. Bole, G. Papa, A multi-objective approach to the application of real-world
production scheduling. Exp. Syst. Appl. 40(15), 5839–5853 (2013) [Online]. Available http://
www.sciencedirect.com/science/article/pii/S0957417413003321
56. M.N. Omidvar, M. Yang, Y. Mei, X. Li, X. Yao, DG2: a faster and more accurate differential
grouping for large-scale black-box optimization. IEEE Trans. Evol. Comput. 21(6), 929–942
(2017)
57. D.M. Cabrera, Evolutionary algorithms for large-scale global optimisation: a snapshot, trends
and challenges. Progr. Artif. Intell. 5(2), 85–89 (2016) [Online]. Available https://doi.org/10.
1007/s13748-016-0082-4
58. X. Li, K. Tang, Z. Suganthan, P.N. Yang, Editorial for the special issue of information sciences
journal (ISJ) on “Nature-inspired algorithms for large scale global optimization”. Inf. Sci. 316,
437–439 (2015)
59. G. Vanderplaats, Very Large Scale Optimization (National Aeronautics and Space Administration, Washington, DC, 2002)
60. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks.
Science 313(5786), 504–507 (2006)
61. Z.H. Zhou, N.V. Chawla, Y. Jin, G.J. Williams, Big data opportunities and challenges:
discussions from data analytics perspectives [discussion forum]. IEEE Comput. Intell. Mag.
9(4), 62–74 (2014)
62. Z. Yang, B. Sendhoff, K. Tang, X. Yao, Target shape design optimization by evolving b-splines
with cooperative coevolution. Appl. Soft Comput. 48(C), 672–682 (2016) [Online]. Available
https://doi.org/10.1016/j.asoc.2016.07.027
63. H.-F. Teng, Y. Chen, W. Zeng, Y.-J. Shi, Q.-H. Hu, A dual-system variable-grain cooperative
coevolutionary algorithm: satellite-module layout design. IEEE Trans. Evol. Comput. 14(3),
438–455 (2010) [Online]. Available http://dx.doi.org/10.1109/TEVC.2009.2033585
64. Y. Wang, J. Huang, W.S. Dong, J.C. Yan, C.H. Tian, M. Li, W.T. Mo, Two-stage based
ensemble optimization framework for large-scale global optimization. Eur. J. Oper. Res.
228(2), 308–320 (2013) [Online]. Available http://www.sciencedirect.com/science/article/pii/
S0377221712009691
65. C. Wang, J. Gao, High-dimensional waveform inversion with cooperative coevolutionary
differential evolution algorithm. IEEE Geosci. Remote Sens. Lett. 9(2), 297–301 (2012)
66. A.W. Mohamed, A.S. Almazyad, Differential evolution with novel mutation and adaptive
crossover strategies for solving large scale global optimization problems. Appl. Comput. Intell.
Soft Comput. 2017, 1–18 (2017)
383
47. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller,
Playing atari with deep reinforcement learning (2013). Preprint. arXiv:1312.5602
48. H. Zhang, J. Lu, Adaptive evolutionary programming based on reinforcement learning. Inf.
Sci. 178(4), 971–984 (2008)
49. X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster. IEEE Trans. Evol. Comput.
3(2), 82–102 (1999)
50. O. Kramer, Evolutionary self-adaptation: a survey of operators and strategy parameters. Evol.
Intell. 3(2), 51–65 (2010)
51. T. Bäck, The interaction of mutation rate, selection, and self-adaptation within a genetic
algorithm, in Proceedings of the 2nd Conference on Parallel Problem Solving from Nature,
PPSN 1992, ed. by R. Männer, B. Manderick (North-Holland, Amsterdam, 1992)
52. J. Brest, M.S. Mauˇ cec, Self-adaptive differential evolution algorithm using population size
reduction and three strategies. Soft Comput. 15(11), 2157–2174 (2011) [Online]. Available
https://doi.org/10.1007/s00500-010-0644-5
53. B. Doerr, C. Witt, J. Yang, Runtime analysis for self-adaptive mutation rates, in Proceedings
of the Genetic and Evolutionary Computation Conference, ECCO ’18 (ACM, New York, NY,
2018), pp. 1475–1482 [Online]. Available http://doi.acm.org/10.1145/3205455.3205569
54. P. Siarry, Z. Michalewicz, Advances in Metaheuristics for Hard Optimization. Natural Computing Series, 1st edn. (Springer, New York, 2007)
55. P. Korošec, U. Bole, G. Papa, A multi-objective approach to the application of real-world
production scheduling. Exp. Syst. Appl. 40(15), 5839–5853 (2013) [Online]. Available http://
www.sciencedirect.com/science/article/pii/S0957417413003321
56. M.N. Omidvar, M. Yang, Y. Mei, X. Li, X. Yao, DG2: a faster and more accurate differential
grouping for large-scale black-box optimization. IEEE Trans. Evol. Comput. 21(6), 929–942
(2017)
57. D.M. Cabrera, Evolutionary algorithms for large-scale global optimisation: a snapshot, trends
and challenges. Progr. Artif. Intell. 5(2), 85–89 (2016) [Online]. Available https://doi.org/10.
1007/s13748-016-0082-4
58. X. Li, K. Tang, Z. Suganthan, P.N. Yang, Editorial for the special issue of information sciences
journal (ISJ) on “Nature-inspired algorithms for large scale global optimization”. Inf. Sci. 316,
437–439 (2015)
59. G. Vanderplaats, Very Large Scale Optimization (National Aeronautics and Space Administration, Washington, DC, 2002)
60. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks.
Science 313(5786), 504–507 (2006)
61. Z.H. Zhou, N.V. Chawla, Y. Jin, G.J. Williams, Big data opportunities and challenges:
discussions from data analytics perspectives [discussion forum]. IEEE Comput. Intell. Mag.
9(4), 62–74 (2014)
62. Z. Yang, B. Sendhoff, K. Tang, X. Yao, Target shape design optimization by evolving b-splines
with cooperative coevolution. Appl. Soft Comput. 48(C), 672–682 (2016) [Online]. Available
https://doi.org/10.1016/j.asoc.2016.07.027
63. H.-F. Teng, Y. Chen, W. Zeng, Y.-J. Shi, Q.-H. Hu, A dual-system variable-grain cooperative
coevolutionary algorithm: satellite-module layout design. IEEE Trans. Evol. Comput. 14(3),
438–455 (2010) [Online]. Available http://dx.doi.org/10.1109/TEVC.2009.2033585
64. Y. Wang, J. Huang, W.S. Dong, J.C. Yan, C.H. Tian, M. Li, W.T. Mo, Two-stage based
ensemble optimization framework for large-scale global optimization. Eur. J. Oper. Res.
228(2), 308–320 (2013) [Online]. Available http://www.sciencedirect.com/science/article/pii/
S0377221712009691
65. C. Wang, J. Gao, High-dimensional waveform inversion with cooperative coevolutionary
differential evolution algorithm. IEEE Geosci. Remote Sens. Lett. 9(2), 297–301 (2012)
66. A.W. Mohamed, A.S. Almazyad, Differential evolution with novel mutation and adaptive
crossover strategies for solving large scale global optimization problems. Appl. Comput. Intell.
Soft Comput. 2017, 1–18 (2017)