Evolutionary computation and genetic algorithms for designing the optimal mine haul road pavements

Anna Tailakova; Alexander Pimonov; Daulet Mahambayev

doi:10.1051/e3sconf/202131503012

All issues

Volume 315 (2021)

E3S Web Conf., 315 (2021) 03012

References

Open Access

Issue		E3S Web Conf. Volume 315, 2021 VI^th International Innovative Mining Symposium


Article Number		03012
Number of page(s)		8
Section		Innovations in Mining Machinery
DOI		https://doi.org/10.1051/e3sconf/202131503012
Published online		28 October 2021

A. Tailakova, A. Pimonov, E3S Web of Conferences, 134, 01007 (2019) [Google Scholar]
V. Shalamanov, S. Shabaev, F. Alama, E3S Web of Conferences, 134, 01013 (2019) [Google Scholar]
MnPAVE User’s Guide. (Minnesota Department of Transportatio, 2012) [Google Scholar]
H. Behbahani, A.M. Khaki, A.A. Amini. Assessment of Perpetual Pavement Performance using Mechanistic-Empirical Pavement Design Guide (M-E PDG) and PerRoad Software Models. (International Conference on Perpetual, Columbus, Ohio, 2009) [Google Scholar]
AASHTOWare Pavement ME User Manual. (Virginia Department of Transportation Pavement Design and Evaluation Section Central Office, Materials Division, 2017) [Google Scholar]
R. Impagliazzo. 10th Annual Structure in Complexity Theory Conference (SCT’95). A personal view of average-case complexity, 134 (1995) [Google Scholar]
R. Marti, Celso C. Ribeiro, Mauricio G.C. Resende. European Journal of Operational Research. Multi-start methods for combinatorial optimization, 226(1), 1 (2013) [Google Scholar]
R. Marti, R. Aceves, M.T. Leon, J.M. Moreno-Vega, and A. Duarte, International Series in Operations Research & Management Science. Intelligent Multi-Start Methods, 272(2018) [Google Scholar]
A. Laaksonen. Competitive Programmer’s Handbook, (2018) [Google Scholar]
D. Bednárek, M. Brabec, M. Kruliš. Information Systems. Improving matrix-based dynamic programming on massively parallel accelerators, 64, 175 (2017) [Google Scholar]
K. Oliver. Genetic Algorithm Essentials. Studies in Computational Intelligence, 679 (2017) [Google Scholar]
E.V. Vasileva, V.S. Doroganov, A.B. Piletskaya, et al. Coke and Chemistry. Predicting the Yield of Coking Products, 60:9, 356 (2017) [Google Scholar]
E.V. Vasileva, V.S. Doroganov, A.B. Piletskaya, et al. Coke and Chemistry. Estimation of the Yield of Coking Productsby a Neural-Network Model, 62:2, 47 (2019) [Google Scholar]
E.V. Vasileva, V.S. Doroganov, A.B. Piletskaya, et al. Coke and Chemistry. Neural-Network Model for Predicting the Yield of Coking Products, 62:2, 40 (2019) [Google Scholar]
S. Ganjefar, M. Tofighi. Engineering Applications of Artificial Intelligence. Training qubit neural network with hybrid genetic algorithm and gradient descent for indirect adaptive controller design, 65, 346 (2017) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] A. Tailakova, A. Pimonov, E3S Web of Conferences, 134, 01007 (2019) [Google Scholar]

[2] V. Shalamanov, S. Shabaev, F. Alama, E3S Web of Conferences, 134, 01013 (2019) [Google Scholar]

[3] MnPAVE User’s Guide. (Minnesota Department of Transportatio, 2012) [Google Scholar]

[4] H. Behbahani, A.M. Khaki, A.A. Amini. Assessment of Perpetual Pavement Performance using Mechanistic-Empirical Pavement Design Guide (M-E PDG) and PerRoad Software Models. (International Conference on Perpetual, Columbus, Ohio, 2009) [Google Scholar]

[5] AASHTOWare Pavement ME User Manual. (Virginia Department of Transportation Pavement Design and Evaluation Section Central Office, Materials Division, 2017) [Google Scholar]

[6] R. Impagliazzo. 10th Annual Structure in Complexity Theory Conference (SCT’95). A personal view of average-case complexity, 134 (1995) [Google Scholar]

[7] R. Marti, Celso C. Ribeiro, Mauricio G.C. Resende. European Journal of Operational Research. Multi-start methods for combinatorial optimization, 226(1), 1 (2013) [Google Scholar]

[8] R. Marti, R. Aceves, M.T. Leon, J.M. Moreno-Vega, and A. Duarte, International Series in Operations Research & Management Science. Intelligent Multi-Start Methods, 272(2018) [Google Scholar]

[9] A. Laaksonen. Competitive Programmer’s Handbook, (2018) [Google Scholar]

[10] D. Bednárek, M. Brabec, M. Kruliš. Information Systems. Improving matrix-based dynamic programming on massively parallel accelerators, 64, 175 (2017) [Google Scholar]

[11] K. Oliver. Genetic Algorithm Essentials. Studies in Computational Intelligence, 679 (2017) [Google Scholar]

[12] E.V. Vasileva, V.S. Doroganov, A.B. Piletskaya, et al. Coke and Chemistry. Predicting the Yield of Coking Products, 60:9, 356 (2017) [Google Scholar]

[13] E.V. Vasileva, V.S. Doroganov, A.B. Piletskaya, et al. Coke and Chemistry. Estimation of the Yield of Coking Productsby a Neural-Network Model, 62:2, 47 (2019) [Google Scholar]

[14] E.V. Vasileva, V.S. Doroganov, A.B. Piletskaya, et al. Coke and Chemistry. Neural-Network Model for Predicting the Yield of Coking Products, 62:2, 40 (2019) [Google Scholar]

[15] S. Ganjefar, M. Tofighi. Engineering Applications of Artificial Intelligence. Training qubit neural network with hybrid genetic algorithm and gradient descent for indirect adaptive controller design, 65, 346 (2017) [Google Scholar]