Open Access
| Issue |
RAIRO-Oper. Res.
Volume 60, Number 2, March-April 2026
|
|
|---|---|---|
| Page(s) | 643 - 684 | |
| DOI | https://doi.org/10.1051/ro/2026007 | |
| Published online | 27 March 2026 | |
- H. Alibrahim and S.A. Ludwig, Hyperparameter optimization: comparing genetic algorithm against grid search and bayesian optimization, in 2021 IEEE Congress on Evolutionary Computation (CEC) (2021) 1551–1559. DOI: 10.1109/CEC45853.2021.9504761. [Google Scholar]
- M. Alicastro, D. Ferone, P. Festa, S. Fugaro and T. Pastore, A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems. Comput. Oper. Res. 131 (2021) 105272. [Google Scholar]
- A. Arbelaez and B. O'Sullivan, Learning a stopping criterion for local search, in Learning and Intelligent Optimization, edited by P. Festa, M. Sellmann and J. Vanschoren. Springer International Publishing, Cham (2016) 3–16. [Google Scholar]
- J. Beasley, A note on solving large p-median problems. Eur. J. Oper. Res. 21 (1985) 270–273. [Google Scholar]
- J. Beasley, An algorithm for set covering problem. Eur. J. Oper. Res. 31 (1987) 85–93. [Google Scholar]
- J.E. Beasley, Or-library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41 (1990) 1069–1072. [CrossRef] [Google Scholar]
- T.C. Belding, The distributed genetic algorithm revisited, in Proceedings of the 6th International Conference on Genetic Algorithms. San Francisco, Morgan Kaufmann Publishers Inc. CA, USA (1995) 114–121. DOI: 10.48550/arXiv.adap-org/9504007>. [Google Scholar]
- C.G.E. Boender and A.H.G. RinnooyKan, Bayesian stopping rules for multistart global optimization methods. Math. Program. 37 (1987) 59–80. [Google Scholar]
- W. Bożejko, A. Burduk, K. Musia and J. Pempera, Neuro-tabu search approach to scheduling in automotive manufacturing. Neurocomputing 452 (2021) 435–442. [Google Scholar]
- L. Breiman, Random forests. Mach. Learn. 45 (2001) 5–32. [Google Scholar]
- R.E. Burkard, S.E. Karisch and F. Rendl, QAPLIB a quadratic assignment problem library. J. Glob. Optim. 10 (1997) 391–403. [Google Scholar]
- J.T.H.A. Cabral, R.A. Araujo, J.P. Nobrega and A.L. deOliveira, Heterogeneous ensemble dynamic selection for software development effort estimation, in 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (2017) 210–217. DOI: 10.1109/ICTAI.2017.00042. [Google Scholar]
- K. Carling and M. Han, GRASP and statistical bounds for heuristic solutions to combinatorial problems. Technical report. Dalarna University (2016). https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A946358&dswid=-5516. [Google Scholar]
- K. Carling and X. Meng, Confidence in heuristic solutions? J. Glob. Optim. 63 (2015) 381–399. [Google Scholar]
- H.D.P. Carvalho, J.F.L. de Oliveira and R.A.A. Fagundes, Dynamic selection of ensemble-based regression models: systematic literature review. Expert Sys. Appl. 290 (2025) 128429. [Google Scholar]
- A. Corominas. On deciding when to stop metaheuristics: properties, rules and termination conditions. Oper. Res. Perspect. 10 (2023) 100283. [Google Scholar]
- J. Demšar, Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (2006) 1–30. [MathSciNet] [Google Scholar]
- A.M. Fathollahi-Fard, M. Hajiaghaei-Keshteli and R. Tavakkoli-Moghaddam, Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput. 24 (2020) 14637–14665. [CrossRef] [Google Scholar]
- G. Felici, D. Ferone, P. Festa, A. Napoletano and T. Pastore, A GRASP for the minimum cost SAT problem, in edited by R. Battiti, D.E. Kvasov and Y.D. Sergeyev. International Conference on Learning and Intelligent Optimization. Springer International Publishing, Cham (2017) 64–78. [Google Scholar]
- T.A. Feo and M.G.C. Resende, Greedy randomized adaptive search procedures. J. Glob. Optim. 6 (1995) 109–133. [CrossRef] [Google Scholar]
- D. Ferone, P. Festa and F. Guerriero, The rainbow steiner tree problem. Comput. Oper. Res. 139 (2022) 105621. [Google Scholar]
- E. Fink and K.B. Pratt, Indexing of Compressed Time Series. World Scientific (2004) 43–65. [Google Scholar]
- S.N. Ghoreishi, A. Clausen and B.N. Joergensen, Termination criteria in evolutionary algorithms: a survey, in Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) IJCCI, INSTICC. SciTePress (2017) 373–384. DOI: 10.5220/0006577903730384. [Google Scholar]
- A. Goli, Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design. Comput. Ind. Eng. 179 (2023) 109211. [CrossRef] [Google Scholar]
- A. Goli, Efficient optimization of robust project scheduling for industry 4.0: a hybrid approach based on machine learning and meta-heuristic algorithms. Int. J. Prod. Econ. 278 (2024) 109427. [Google Scholar]
- A. Goli and E.B. Tirkolaee, Designing a portfolio-based closed-loop supply chain network for dairy products with a financial approach: accelerated benders decomposition algorithm. Comput. Oper. Res. 155 (2023) 106244. [CrossRef] [Google Scholar]
- A. Goli, A. Ala and M. Hajiaghaei-Keshteli, Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem. Expert Sys. Appl. 213 (2023) 119077. [Google Scholar]
- A. Goli, A. Ala and S. Mirjalili, A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty. Ann. Oper. Res. 328 (2023) 493–530. [CrossRef] [MathSciNet] [Google Scholar]
- C.R. Harris, K.J. Millman, S.J. vander Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N.J. Smith, R. Kern, M. Picus, S. Hoyer, M.H. van Kerkwijk, M. Brett, A. Haldane, J. Fernándezdel Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke and T.E. Oliphant, Array programming with NumPy. Nature 585 (2020) 357–362. [CrossRef] [PubMed] [Google Scholar]
- S. Herbold, Autorank: a python package for automated ranking of classifiers. J. Open Source Softw. 5 (2020) 2173. [Google Scholar]
- J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press (1992). [Google Scholar]
- J.D. Hunter, Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9 (2007) 90–95. [NASA ADS] [CrossRef] [Google Scholar]
- D. Karaboga, B. Akay and N. Karaboga, A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm. Cogent Eng. 7 (2020) 1855741. [Google Scholar]
- W. Li, G.-G. Wang and A.H. Gandomi, A survey of learning-based intelligent optimization algorithms. Arch. Comput. Methods Eng. 28 (2021) 3781–3799. [CrossRef] [MathSciNet] [Google Scholar]
- S. Liu, H. Wang, W. Peng and W. Yao, Surrogate-assisted evolutionary algorithms for expensive combinatorial optimization: a survey. Complex Intell. Syst. 10 (2024) 5933–5949. [Google Scholar]
- S. Lloyd, Least squares quantization in PCM. IEEE Trans. Inf. Theory 28 (2082) 129–137. [Google Scholar]
- G. Luque and E. Alba, Parallel Genetic Algorithms: Theory and Real World Applications. Studies in Computational Intelligence. Springer, Berlin, Germany, 2011 edition (2011). DOI: 10.1007/978-3-642-22084-5. [Google Scholar]
- G.C. Mattos, Machine learning-based probabilistic stopping rule for the GRASP metaheuristic. Master's dissertation, Federal University of Rio de Janeiro, Rio de Janeiro, RJ (2021). https://www.cos.ufrj.br/index.php/pt-BR/publicacoes-pesquisa/details/15/3022. [Google Scholar]
- G.C. Mattos, GRASP C++ code for the p-median problem for “Ensemble machine learning-based stopping rule for greedy randomized adaptive search procedure” (2026). https://github.com/GuilhermeCaeiro/grasp_pmedian_cpp. [Google Scholar]
- G.C. Mattos, F.M.G. França, L.G. Simonetti and P.M.V. Lima, AIISRAI inspired stopping rule for GRASP metaheuristic, in Anais do Simpósio Brasileiro de Pesquisa Operacional. Vol. 53. João Pessoa Paraíba, Brasil, Galoa (2021). DOI: 10.59254/sbpo-2021-131575. [Google Scholar]
- G.C. Mattos, L.A.D. LusquinoFilho, L.G. Simonetti and P.M.V. Lima, Raw dataset (v1.0) for “Ensemble machine learning-based stopping rule for greedy randomized adaptive search procedure” (2026). https://doi.org/10.5281/zenodo.15522802. [Google Scholar]
- A. Morales-Hernández, I. VanNieuwenhuyse and S. Rojas Gonzalez, A survey on multi-objective hyperparameter optimization algorithms for machine learning. Artif. Intell. Rev. 56 (2023) 8043–8093. [Google Scholar]
- T.J.M. Moura, G.D.C. Cavalcanti and L.S. Oliveira, Evaluating competence measures for dynamic regressor selection, in 2019 International Joint Conference on Neural Networks (IJCNN). IEEE (2019) 1–8. DOI: 10.1109/IJCNN.2019.8851835. [Google Scholar]
- L.A. Neves, Critérios de parada baseados em probabilidade bayesiana aplicados a heurísticas GRASP: um estudo experimental. Master's thesis, Federal University of the Stat of Rio de Janeiro, Rio de Janeiro, RJ. May (2009). http://www.repositorio-bc.unirio.br:8080/xmlui/handle/unirio/12807. [Google Scholar]
- L.A. Neves, A.C.F. Alvim and M.G.C. Resende, Implementação e teste de critérios de parada para heurísticas GRASP, in Anais Do XLI Simposio Brasileiro De Pesquisa Operacional (SBPO). Porto Seguro (2009) 1989–1999. https://www.researchgate.net/publication/255654512. [Google Scholar]
- V.D. Noghin, A combined approach to reducing the pareto set using linear or multiplicative scalarization. Sci. Tech. Inf. Process. 44 (2017) 373–378. [Google Scholar]
- C.A.S. Oliveira, P.M. Pardalos and M.G.C. Resende, GRASP with path-relinking for the quadratic assignment problem, in Experimental and Efficient Algorithms, edited by C.C. Ribeiro and S.L. Martins. Springer Berlin Heidelberg, Berlin, Heidelberg (2004) 356–368. [Google Scholar]
- C. Orsenigo and C. Vercellis, Bayesian stopping rules for greedy randomized procedures. J. Glob. Optim. 36 (2006) 365–377. [Google Scholar]
- T. Pastore, C. Menna and D. Asprone, Bézier-based biased random-key genetic algorithm to address printability constraints in the topology optimization of concrete structures. Struct. Multidiscipl. Optim. 65 (2022) 64. [Google Scholar]
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay, Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12 (2011) 2825–2830. https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html. [MathSciNet] [Google Scholar]
- L.S. Pessoa, M.G.C. Resende and C.C. Ribeiro, Experiments with LAGRASP heuristic for set k-covering. Optim. Lett. 5 (2011) 407–419. [Google Scholar]
- L.S. Pessoa, M.G. Resende and C.C. Ribeiro, A hybrid Lagrangean heuristic with GRASP and path-relinking for set k-covering. Comput. Oper. Res. 40 (2013) 3132–3146. [Google Scholar]
- M. Prais and C.C. Ribeiro, Reactive GRASP: an application to a matrix decomposition problem in TDMA traffic assignment. INFORMS J. Comput. 12 (2000) 164–176. [Google Scholar]
- A. Ramdas, N.G. Trillos and M. Cuturi, On Wasserstein two-sample testing and related families of nonparametric tests. Entropy 19 (2017) 47. [Google Scholar]
- M.G. Resende and C.C. Ribeiro, Optimization by GRASP: Greedy Randomized Adaptive Search Procedures. Springer New York, New York, NY (2016). DOI: 10.1007/978-1-4939-6530-4. [Google Scholar]
- M.G. Resendel and C.C. Ribeiro, GRASP with Path-Relinking: Recent Advances and Applications. Springer US, Boston, MA (2005) 29–63. [Google Scholar]
- C.C. Ribeiro, I. Rosseti and R.C. Souza, Probabilistic stopping rules for GRASP heuristics and extensions. Int. Trans. Oper. Res. 20 (2013) 301–323. [Google Scholar]
- S. Skipper and P. Josef, Statsmodels: econometric and statistical modeling with python. SciPy 7 (2010) 92–96. [Google Scholar]
- P.N. Smyrlis, D.C. Tsouros and M.G. Tsipouras, Constrained k-means classification. Eng. Technol. Appl. Sci. Res. 8 (2018) 3203–3208. [Google Scholar]
- M. Sulaman, M. Golabi, M. Essaid, M. Brévilliers, J. Lepagnot and L. Idoumghar, Random forest assisted differential evolution for multi-server congested p-median problem, in 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI) (2023) 404–409. DOI: 10.1109/ICTAI59109.2023.00065. [Google Scholar]
- Y. Sun, S. Wang, Y. Shen, X. Li, A.T. Ernst and M. Kirley, Boosting ant colony optimization via solution prediction and machine learning. Comput. Oper. Res. 143 (2022) 105769. [Google Scholar]
- F. Tao, Y. Laili and L. Zhang, Brief History and Overview of Intelligent Optimization Algorithms. Springer International Publishing, Cham (2015) 3–33. [Google Scholar]
- K. Taunk, S. De, S. Verma and A. Swetapadma, A brief review of nearest neighbor algorithm for learning and classification, in 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (2019) 1255–1260. DOI: 10.1109/ICCS45141.2019.9065747. [Google Scholar]
- The pandas development team. Pandas (2024) https://doi.org/10.5281/zenodo.13819579. [Google Scholar]
- P. Virtanen, R. Gommers, T.E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S.J. van der Walt, M. Brett, J. Wilson, K.J. Millman, N. Mayorov, A.R.J. Nelson, E. Jones, R. Kern, E. Larson, C.J. Carey, İ. Polat, Y. Feng, E.W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E.A. Quintero, C.R. Harris, A.M. Archibald, A.H. Ribeiro, F. Pedregosa, P. van Mulbregt and SciPy 1.0 Contributors, SciPy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17 (2020) 261–272. [NASA ADS] [CrossRef] [Google Scholar]
- A.S. Wicaksono and A. Afif, Hyper parameter optimization using genetic algorithm on machine learning methods for online news popularity prediction. Int. J. Adv. Comput. Sci. Appl. 9 (2018). DOI: 10.14569/IJACSA.2018.091238. [Google Scholar]
- S.C. Yusta, Different metaheuristic strategies to solve the feature selection problem. Pattern Recognit. Lett. 30 (2009) 525–534. [Google Scholar]
- M. ˇZiˇzovi´c, M. ˇZiˇzovi´c, N. Damljanovi´c and K. Pavlovi´c, Multiplicative method based on expected criteria values. Rep. Mech. Eng. 4 (2023) 317–325. [Google Scholar]
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