EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 59 (2024) ITM Web Conf., 59 (2024) 02020 References
Open Access
Issue
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
Article Number 02020
Number of page(s) 11
Section Interdisciplinary Mathematical Modeling and Applications
DOI https://doi.org/10.1051/itmconf/20245902020
Published online 25 January 2024
  1. E. Semenkin, M. Semenkina, LNCS. Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator 7331, 414–421 (2012) [Google Scholar]
  2. E. Semenkin, M. Semenkina, IEEE Congress on Evolutionary Computation, CEC 2012. Self-configuring genetic programming algorithm with modified uniform crossover (2012) [Google Scholar]
  3. L. Lipinskiy, E. Semenkin, Bulletin of the Siberian State Aerospace University 3(10), 22–26 (2006) [Google Scholar]
  4. The official home of the Python Programming Language [Internet]. [cited 2023 Aug 20]. Available from: https://www.python.org/ [Google Scholar]
  5. F.A. Fortin, F.M. De Rainville, M.A. Gardner, M GC Parizeau, Journal of Machine Learning Research. DEAP: Evolutionary algorithms made easy, 2171–2175 (2012) [Google Scholar]
  6. P. Virtanen, R. Gommers, T.E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau et al., Nat Methods. SciPy 1.0: fundamental algorithms for scientific computing in Python 17(3), 261–272 (2020) [Google Scholar]
  7. A.F. Gad, PyGAD: An Intuitive Genetic Algorithm Python Library (2021). Available from: http://arxiv.org/abs/2106.06158 [Google Scholar]
  8. J. Blank, K. Deb, IEEE Access. Pymoo: Multi-Objective Optimization in Python 8, 89497–8509 (2020) [Google Scholar]
  9. Welcome to gplearn’s documentation! [Internet] (2023). Available from: https://gplearn.readthedocs.io/en/stable/ [Google Scholar]
  10. Thefittest: Implementation of data mining methods that use evolutionary algorithms [Internet] (2023). Available from: https://github.com/sherstpasha/thefittest; [Google Scholar]
  11. thefittest PyPI [Internet] (2023). Available from: https://pypi.org/project/thefittest/ [Google Scholar]
  12. Optional Static Typing for Python [Internet] (2023). Available from: https://mypy-lang.org/ [Google Scholar]
  13. pytest: helps you write better programs [Internet] (2023). Available from: https://docs.pytest.org/en/7.4.x/; [Google Scholar]
  14. PEP 8: The Style Guide for Python Code [Internet] (2023). Available from: https://pep8.org/; [Google Scholar]
  15. C.R. Harris, K.J. Millman, S.J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau et al., Nature. Nature Research. Array programming with NumPy 585, 357–362 (2020) [Google Scholar]
  16. Numba documentation [Internet] (2023). Available from: https://numba.readthedocs.io/en/stable/index.html; [Google Scholar]
  17. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine Learning in Python (2012). Available from: http://arxiv.org/abs/1201.0490 [Google Scholar]
  18. A.A. Hagberg, Los lanlgov, D.A. Schult, P.J. Swart, Exploring Network Structure, Dynamics, and Function using NetworkX [Internet] (2008). Available from: http://conference.scipy.org/proceedings/SciPy2008/paper_2 [Google Scholar]
  19. Iris. UCI Machine Learning Repository [Internet] (2023). Available from: https://archive.ics.uci.edu/dataset/53/iris; [Google Scholar]
  20. J. Liang, K. Deb, Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization [Internet] (2005). Available from: https://www.researchgate.net/publication/235710019 [Google Scholar]
  21. Wine. UCI Machine Learning Repository [Internet] (2023). Available from: https://archive.ics.uci.edu/dataset/109/wine [Google Scholar]
  22. R. Tanabe, A. Fukunaga, Evolutionary Computation (CEC). Success-history based parameter adaptation for Differential Evolution, 71–78 (2013) [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.