{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:48:03Z","timestamp":1771260483181,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T00:00:00Z","timestamp":1708214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Foundation Ireland","award":["18\/CRT\/6049"],"award-info":[{"award-number":["18\/CRT\/6049"]}]},{"name":"Science Foundation Ireland","award":["21\/FFP-P\/10065"],"award-info":[{"award-number":["21\/FFP-P\/10065"]}]},{"name":"Science Foundation Ireland","award":["465704\/2014-0"],"award-info":[{"award-number":["465704\/2014-0"]}]},{"name":"Science Foundation Ireland","award":["425509\/2018-4"],"award-info":[{"award-number":["425509\/2018-4"]}]},{"name":"Science Foundation Ireland","award":["APQ-00870-17"],"award-info":[{"award-number":["APQ-00870-17"]}]},{"name":"Brazilian Research Agencies: CNPq\/INERGE","award":["18\/CRT\/6049"],"award-info":[{"award-number":["18\/CRT\/6049"]}]},{"name":"Brazilian Research Agencies: CNPq\/INERGE","award":["21\/FFP-P\/10065"],"award-info":[{"award-number":["21\/FFP-P\/10065"]}]},{"name":"Brazilian Research Agencies: CNPq\/INERGE","award":["465704\/2014-0"],"award-info":[{"award-number":["465704\/2014-0"]}]},{"name":"Brazilian Research Agencies: CNPq\/INERGE","award":["425509\/2018-4"],"award-info":[{"award-number":["425509\/2018-4"]}]},{"name":"Brazilian Research Agencies: CNPq\/INERGE","award":["APQ-00870-17"],"award-info":[{"award-number":["APQ-00870-17"]}]},{"name":"CNPq","award":["18\/CRT\/6049"],"award-info":[{"award-number":["18\/CRT\/6049"]}]},{"name":"CNPq","award":["21\/FFP-P\/10065"],"award-info":[{"award-number":["21\/FFP-P\/10065"]}]},{"name":"CNPq","award":["465704\/2014-0"],"award-info":[{"award-number":["465704\/2014-0"]}]},{"name":"CNPq","award":["425509\/2018-4"],"award-info":[{"award-number":["425509\/2018-4"]}]},{"name":"CNPq","award":["APQ-00870-17"],"award-info":[{"award-number":["APQ-00870-17"]}]},{"name":"FAPEMIG","award":["18\/CRT\/6049"],"award-info":[{"award-number":["18\/CRT\/6049"]}]},{"name":"FAPEMIG","award":["21\/FFP-P\/10065"],"award-info":[{"award-number":["21\/FFP-P\/10065"]}]},{"name":"FAPEMIG","award":["465704\/2014-0"],"award-info":[{"award-number":["465704\/2014-0"]}]},{"name":"FAPEMIG","award":["425509\/2018-4"],"award-info":[{"award-number":["425509\/2018-4"]}]},{"name":"FAPEMIG","award":["APQ-00870-17"],"award-info":[{"award-number":["APQ-00870-17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.<\/jats:p>","DOI":"10.3390\/a17020087","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T05:42:46Z","timestamp":1708494166000},"page":"87","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Transfer Reinforcement Learning for Combinatorial Optimization Problems"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3679-3298","authenticated-orcid":false,"given":"Gleice Kelly Barbosa","family":"Souza","sequence":"first","affiliation":[{"name":"Technologic and Exact Center, Federal University of Rec\u00f4ncavo da Bahia, R. Rui Barbosa, Cruz das Almas 44380-000, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0018-8870","authenticated-orcid":false,"given":"Samara Oliveira Silva","family":"Santos","sequence":"additional","affiliation":[{"name":"Hamilton Institute, Maynooth University, W23VP22 Maynooth, Co. Kildare, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2136-9870","authenticated-orcid":false,"given":"Andr\u00e9 Luiz Carvalho","family":"Ottoni","sequence":"additional","affiliation":[{"name":"Technologic and Exact Center, Federal University of Rec\u00f4ncavo da Bahia, R. Rui Barbosa, Cruz das Almas 44380-000, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4395-4640","authenticated-orcid":false,"given":"Marcos Santos","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Federal University of S\u00e3o Jo\u00e3o del-Rei, Pra\u00e7a Frei Orlando, S\u00e3o Jo\u00e3o del Rei 36309-034, Minas Gerais, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9573-8424","authenticated-orcid":false,"given":"Daniela Carine Ramires","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Federal University of S\u00e3o Jo\u00e3o del-Rei, Pra\u00e7a Frei Orlando, S\u00e3o Jo\u00e3o del Rei 36309-034, Minas Gerais, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5841-2193","authenticated-orcid":false,"given":"Erivelton Geraldo","family":"Nepomuceno","sequence":"additional","affiliation":[{"name":"Centre for Ocean Energy Research, Department of Electronic Engineering, Maynooth University, W23VP22 Maynooth, Co. Kildare, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s10844-022-00738-0","article-title":"Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks","volume":"60","author":"Ghanem","year":"2023","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF00992698","article-title":"Technical note Q-learning","volume":"8","author":"Watkins","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_3","unstructured":"Russell, S.J., and Norving, P. (2013). Artificial Intelligence, Pearson. [3rd ed.]."},{"key":"ref_4","unstructured":"Sutton, R., and Barto, A. (2018). Reinforcement Learning: An Introduction, MIT Press. [2nd ed.]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.apenergy.2018.11.002","article-title":"Reinforcement learning for demand response: A review of algorithms and modeling techniques","volume":"235","author":"Nagy","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105400","DOI":"10.1016\/j.cor.2021.105400","article-title":"Reinforcement learning for combinatorial optimization: A survey","volume":"134","author":"Mazyavkina","year":"2021","journal-title":"Comput. Oper. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ruiz-Serra, J., and Harr\u00e9, M.S. (2023). Inverse Reinforcement Learning as the Algorithmic Basis for Theory of Mind: Current Methods and Open Problems. Algorithms, 16.","DOI":"10.3390\/a16020068"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"De\u00e1k, S., Levine, P., Pearlman, J., and Yang, B. (2023). Reinforcement Learning in a New Keynesian Model. Algorithms, 16.","DOI":"10.3390\/a16060280"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Engelhardt, R.C., Oedingen, M., Lange, M., Wiskott, L., and Konen, W. (2023). Iterative Oblique Decision Trees Deliver Explainable RL Models. Algorithms, 16.","DOI":"10.20944\/preprints202304.1162.v1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1613\/jair.1.13596","article-title":"Automated Reinforcement Learning (AutoRL): A Survey and Open Problems","volume":"74","author":"Rajan","year":"2022","journal-title":"J. Artif. Intell. Res."},{"key":"ref_11","unstructured":"Afshar, R.R., Zhang, Y., Vanschoren, J., and Kaymak, U. (2022). Automated Reinforcement Learning: An Overview. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Brazdil, P., van Rijn, J.N., Soares, C., and Vanschoren, J. (2022). Metalearning: Applications to Automated Machine Learning and Data Mining, Springer Nature.","DOI":"10.1007\/978-3-030-67024-5"},{"key":"ref_13","first-page":"2962","article-title":"Efficient and Robust Automated Machine Learning","volume":"Volume 28","author":"Cortes","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tuggener, L., Amirian, M., Rombach, K., Lorwald, S., Varlet, A., Westermann, C., and Stadelmann, T. (2019, January 14). Automated Machine Learning in Practice: State of the Art and Recent Results. Proceedings of the 2019 6th Swiss Conference on Data Science (SDS), Bern, Switzerland.","DOI":"10.1109\/SDS.2019.00-11"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5374","DOI":"10.1109\/TNNLS.2021.3070584","article-title":"Multiagent Meta-Reinforcement Learning for Adaptive Multipath Routing Optimization","volume":"33","author":"Chen","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dai, H., Chen, P., and Yang, H. (2022). Metalearning-Based Fault-Tolerant Control for Skid Steering Vehicles under Actuator Fault Conditions. Sensors, 22.","DOI":"10.3390\/s22030845"},{"key":"ref_17","first-page":"1633","article-title":"Transfer Learning for Reinforcement Learning Domains: A Survey","volume":"10","author":"Taylor","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","unstructured":"Carroll, J.L., and Peterson, T. (2002, January 24\u201327). Fixed vs. Dynamic Sub-Transfer in Reinforcement Learning. Proceedings of the International Conference on Machine Learning and Applications, Las Vegas, NV, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2706","DOI":"10.1109\/LRA.2021.3057050","article-title":"Transfer Reinforcement Learning Across Homotopy Classes","volume":"6","author":"Cao","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_20","unstructured":"Peterson, T.S., Owens, N.E., and Carroll, J.L. (2001, January 21\u201326). Towards automatic shaping in robot navigation. Proceedings of the 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), Seoul, Republic of Korea."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JAS.2014.7004683","article-title":"Reinforcement learning transfer based on subgoal discovery and subtask similarity","volume":"1","author":"Wang","year":"2014","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/TCDS.2016.2607018","article-title":"A Reinforcement Learning Architecture That Transfers Knowledge Between Skills When Solving Multiple Tasks","volume":"11","author":"Tommasino","year":"2019","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Arnekvist, I., Kragic, D., and Stork, J.A. (2019, January 20\u201324). VPE: Variational Policy Embedding for Transfer Reinforcement Learning. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793556"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gao, D., Wang, S., Yang, Y., Zhang, H., Chen, H., Mei, X., Chen, S., and Qiu, J. (2024). An Intelligent Control Method for Servo Motor Based on Reinforcement Learning. Algorithms, 17.","DOI":"10.3390\/a17010014"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TEVC.2017.2664665","article-title":"An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems","volume":"21","author":"Hou","year":"2017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1613\/jair.1.11396","article-title":"A survey on transfer learning for multiagent reinforcement learning systems","volume":"64","year":"2019","journal-title":"J. Artif. Intell. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"39273","DOI":"10.1109\/ACCESS.2020.2976121","article-title":"Multi-AUV Collaborative Target Recognition Based on Transfer-Reinforcement Learning","volume":"8","author":"Cai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1007\/s40747-021-00444-4","article-title":"Reinforcement learning for the traveling salesman problem with refueling","volume":"8","author":"Ottoni","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gambardella, L.M., and Dorigo, M. (1995, January 9\u201312). Ant-Q: A reinforcement learning approach to the traveling salesman problem. Proceedings of the 12th International Conference on Machine Learning, Tahoe, CA, USA.","DOI":"10.1016\/B978-1-55860-377-6.50039-6"},{"key":"ref_30","unstructured":"Bianchi, R.A.C., Ribeiro, C.H.C., and Costa, A.H.R. (2009, January 13). On the relation between Ant Colony Optimization and Heuristically Accelerated Reinforcement Learning. Proceedings of the 1st International Workshop on Hybrid Control of Autonomous System, Pasadena, CA, USA."},{"key":"ref_31","unstructured":"J\u00fanior, F.C.D.L., Neto, A.D.D., and De Melo, J.D. (2010). Traveling Salesman Problem, Theory and Applications, IntechOpen."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4351","DOI":"10.1109\/TLA.2016.7786315","article-title":"Hierarchical Reinforcement Learning and Parallel Computing Applied to the k-server Problem","volume":"14","author":"Costa","year":"2016","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1007\/s00521-017-2880-4","article-title":"A Hybrid Algorithm Using a Genetic Algorithm and Multiagent Reinforcement Learning Heuristic to Solve the Traveling Salesman Problem","volume":"30","author":"Alipour","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.eswa.2019.06.015","article-title":"Deep reinforcement learning applied to the k-server problem","volume":"135","author":"Lins","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/TLA.2020.9049466","article-title":"Development of a Pedagogical Graphical Interface for the Reinforcement Learning","volume":"18","year":"2020","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.eswa.2019.04.056","article-title":"A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems","volume":"131","author":"Silva","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1007\/s00500-019-04206-w","article-title":"Tuning of Reinforcement Learning Parameters Applied to SOP Using the Scott\u2013Knott Method","volume":"24","author":"Ottoni","year":"2020","journal-title":"Soft Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/0377-2217(88)90333-5","article-title":"An inexact algorithm for the sequential ordering problem","volume":"37","author":"Escudero","year":"1988","journal-title":"Eur. J. Oper. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1287\/ijoc.12.3.237.12636","article-title":"An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem","volume":"12","author":"Gambardella","year":"2000","journal-title":"Informs J. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ejor.2015.11.001","article-title":"Stronger multi-commodity flow formulations of the (capacitated) sequential ordering problem","volume":"251","author":"Letchford","year":"2016","journal-title":"Eur. J. Oper. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cor.2017.04.012","article-title":"An improved Ant Colony System for the Sequential Ordering Problem","volume":"86","author":"Skinderowicz","year":"2017","journal-title":"Comput. Oper. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/BF00339943","article-title":"\u201cNeural\u201d computation of decisions in optimization problems","volume":"52","author":"Hopfield","year":"1985","journal-title":"Biol. Cybern."},{"key":"ref_43","unstructured":"J\u00e4ger, G., and Molitor, P. (2008, January 21\u201324). Algorithms and experimental study for the traveling salesman problem of second order. Proceedings of the Second International Conference, COCOA 2008, St. John\u2019s, NL, Canada. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 5165 LNCS."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Takashima, Y., and Nakamura, Y. (2021, January 22\u201326). Theoretical and Experimental Analysis of Traveling Salesman Walk Problem. Proceedings of the 2021 IEEE Asia Pacific Conference on Circuit and Systems (APCCAS), Penang, Malaysia.","DOI":"10.1109\/APCCAS51387.2021.9687781"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s44196-023-00385-5","article-title":"Solving Traveling Salesman Problem Using Parallel River Formation Dynamics Optimization Algorithm on Multi-core Architecture Using Apache Spark","volume":"17","author":"Alhenawi","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10589-015-9725-9","article-title":"An exact algorithm for the sequential ordering problem and its application to switching energy minimization in compilers","volume":"61","author":"Shobaki","year":"2015","journal-title":"Comput. Optim. Appl."},{"key":"ref_47","unstructured":"Libralesso, L., Bouhassoun, A., Cambazard, H., and Jost, V. (2019). Tree search algorithms for the Sequential Ordering Problem. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s10845-013-0811-5","article-title":"An ant colony optimization approach for the parallel machine scheduling problem with outsourcing allowed","volume":"26","year":"2015","journal-title":"J. Intell. Manuf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1287\/ijoc.3.4.376","article-title":"TSPLIB\u2014A Traveling Salesman Problem Library","volume":"3","author":"Reinelt","year":"1991","journal-title":"ORSA J. Comput."},{"key":"ref_50","unstructured":"Reinelt, G. (1995). Tsplib95, University Heidelberg."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s40747-020-00138-3","article-title":"Improving ant colony optimization algorithm with epsilon greedy and Levy flight","volume":"7","author":"Liu","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_52","unstructured":"Goldbarg, M.C., and Luna, H. (2015). Combinatorial Optimization and Linear Programming: Models and Algorithms, Elsevier Publishing House."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4939","DOI":"10.1016\/j.eswa.2014.01.040","article-title":"Reactive Search strategies using Reinforcement Learning, local search algorithms and Variable Neighborhood Search","volume":"41","author":"Aloise","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Almeida, C.P.d., Gon\u00e7alves, R.A., Goldbarg, E.F., Goldbarg, M.C., and Delgado, M.R. (2014, January 18\u201322). Transgenetic Algorithms for the Multi-objective Quadratic Assignment Problem. Proceedings of the 2014 Brazilian Conference on Intelligent Systems, Sao Paulo, Brazil.","DOI":"10.1109\/BRACIS.2014.63"},{"key":"ref_55","unstructured":"Bengio, Y., Lodi, A., and Prouvost, A. (2018). Machine Learning for Combinatorial Optimization: A Methodological Tour d\u2019Horizon. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.artint.2015.05.008","article-title":"Transferring knowledge as heuristics in reinforcement learning: A case-based approach","volume":"226","author":"Bianchi","year":"2015","journal-title":"Artif. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.endm.2013.05.101","article-title":"A tabu search approach for the prize collecting traveling salesman problem","volume":"41","author":"Pedro","year":"2013","journal-title":"Electron. Notes Discret. Math."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Montemanni, R., and Dell\u2019Amico, M. (2023). Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming. Algorithms, 16.","DOI":"10.3390\/a16010040"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/0305-0548(83)90030-8","article-title":"Routing and Scheduling of Vehicles and Crews\u2014The State of the Art","volume":"10","author":"Bodin","year":"1983","journal-title":"Comput. Oper. Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Majidi, F., Openja, M., Khomh, F., and Li, H. (2022, January 2\u20137). An Empirical Study on the Usage of Automated Machine Learning Tools. Proceedings of the 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME), Limassol, Cyprus.","DOI":"10.1109\/ICSME55016.2022.00014"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"18383","DOI":"10.1007\/s00500-023-09103-x","article-title":"Automated hyperparameter tuning for crack image classification with deep learning","volume":"27","author":"Ottoni","year":"2023","journal-title":"Soft Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"29226","DOI":"10.34117\/bjdv5n12-082","article-title":"PBIL AutoEns: An Automated Machine Learning Tool integrated to the Weka ML Platform","volume":"5","author":"Barreto","year":"2019","journal-title":"Braz. J. Dev."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chauhan, K., Jani, S., Thakkar, D., Dave, R., Bhatia, J., Tanwar, S., and Obaidat, M.S. (2020, January 5\u20137). Automated Machine Learning: The New Wave of Machine Learning. Proceedings of the 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India.","DOI":"10.1109\/ICIMIA48430.2020.9074859"},{"key":"ref_64","unstructured":"Olson, R.S., and Moore, J.H. (2016, January 24). TPOT: A tree-based pipeline optimization tool for automating machine learning. Proceedings of the Workshop on Automatic Machine Learning, New York, NY, USA."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"106300","DOI":"10.1016\/j.engappai.2023.106300","article-title":"Meta-GNAS: Meta-reinforcement learning for graph neural architecture search","volume":"123","author":"Li","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ottoni, L.T.C., Ottoni, A.L.C., and Cerqueira, J.d.J.F. (2023). A Deep Learning Approach for Speech Emotion Recognition Optimization Using Meta-Learning. Electronics, 12.","DOI":"10.3390\/electronics12234859"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ins.2019.06.005","article-title":"A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers","volume":"501","author":"Mantovani","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Automated Machine Learning: Methods, Systems, Challenges, Springer. Available online: http:\/\/automl.org\/book.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, F., and Veloso, M. (2006, January 8\u201312). Probabilistic policy reuse in a reinforcement learning agent. Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, Hakodate, Japan.","DOI":"10.1145\/1160633.1160762"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Feng, Y., Wang, G., Liu, Z., Feng, R., Chen, X., and Tai, N. (2019). An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning. Algorithms, 12.","DOI":"10.3390\/a12120271"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Pavlyuk, D. (2020). Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting. Algorithms, 13.","DOI":"10.3390\/a13020039"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Islam, M.M., Hossain, M.B., Akhtar, M.N., Moni, M.A., and Hasan, K.F. (2022). CNN Based on Transfer Learning Models Using Data Augmentation and Transformation for Detection of Concrete Crack. Algorithms, 15.","DOI":"10.3390\/a15080287"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Surendran, R., Chihi, I., Anitha, J., and Hemanth, D.J. (2023). Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine. Algorithms, 16.","DOI":"10.3390\/a16090430"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Pavliuk, O., Mishchuk, M., and Strauss, C. (2023). Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform. Algorithms, 16.","DOI":"10.3390\/a16020077"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Durgut, R., Aydin, M.E., and Rakib, A. (2022). Transfer Learning for Operator Selection: A Reinforcement Learning Approach. Algorithms, 15.","DOI":"10.3390\/a15010024"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1007\/s40313-018-0374-y","article-title":"A Response Surface Model Approach to Parameter Estimation of Reinforcement Learning for the Travelling Salesman Problem","volume":"29","author":"Ottoni","year":"2018","journal-title":"J. Control. Autom. Electr. Syst."},{"key":"ref_77","unstructured":"Montgomery, D.C. (2017). Design and Analysis of Experiments, John Wiley & Sons.. [9th ed.]."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1007\/978-3-642-04898-2_326","article-title":"Kolmogorov-Smirnov Test","volume":"1","author":"Lopes","year":"2011","journal-title":"Int. Encycl. Stat. Sci."},{"key":"ref_79","first-page":"86","article-title":"AutoRL-TSP-RSM: Automated reinforcement learning system with response surface methodology for the traveling salesman problem","volume":"13","author":"Souza","year":"2021","journal-title":"Braz. J. Appl. Comput."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1016\/j.cor.2010.10.014","article-title":"A hybrid particle swarm optimization approach for the sequential ordering problem","volume":"38","author":"Anghinolfi","year":"2011","journal-title":"Comput. Oper. Res."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/2\/87\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:01:52Z","timestamp":1760104912000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/2\/87"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,18]]},"references-count":80,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["a17020087"],"URL":"https:\/\/doi.org\/10.3390\/a17020087","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,18]]}}}