{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T20:57:48Z","timestamp":1758056268558,"version":"3.44.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051752","type":"print"},{"value":"9783032051769","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05176-9_35","type":"book-chapter","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:41:19Z","timestamp":1757943679000},"page":"452-463","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Execution to\u00a0Representation: Capturing Metaheuristic Behaviour via\u00a0Graph-Derived Meta-features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4565-5276","authenticated-orcid":false,"given":"Jos\u00e9 Carlos","family":"Souza Pacheco J\u00fanior","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4100-5930","authenticated-orcid":false,"given":"Enrico Uchoa","family":"da Silva Leal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0556-9318","authenticated-orcid":false,"given":"Nicolly Carvalho","family":"Cutrim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2230-9573","authenticated-orcid":false,"given":"Guilherme Alberto","family":"Sousa Ribeiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1997-4983","authenticated-orcid":false,"given":"Bruno Feres","family":"de Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1499-5307","authenticated-orcid":false,"given":"Alexandre C\u00e9sar Muniz","family":"de Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Allamanis, M.: Graph Neural Networks in Program Analysis, pp. 483\u2013497. Springer, Singapore (2022)","DOI":"10.1007\/978-981-16-6054-2_22"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Barnhart, C., Jiang, H., Marla, L.: Optimization approaches to airline industry challenges: airline schedule planning and recovery. In: Models and Algorithms for Optimization in Logistics (2009)","DOI":"10.1002\/9780470744734.ch7"},{"key":"35_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/0471787779","volume-title":"Nonlinear Programming: Theory and Algorithms","author":"MS Bazaraa","year":"2006","unstructured":"Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory and Algorithms. Wiley, Hoboken (2006)"},{"key":"35_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101501","volume":"86","author":"HTT Binh","year":"2024","unstructured":"Binh, H.T.T., Bang, B.H., Thai, N.D., Ha, P.B., et al.: A multi-population multi-tasking variable neighborhood search algorithm with diversity enhancements for inter-domain path computation problem. Swarm Evol. Comput. 86, 101501 (2024)","journal-title":"Swarm Evol. Comput."},{"key":"35_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"S Boyd","year":"2004","unstructured":"Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)"},{"key":"35_CR6","volume-title":"Metalearning: Applications to Data Mining","author":"P Brazdil","year":"2008","unstructured":"Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining, 1st edn. Springer, Cham (2008)","edition":"1"},{"key":"35_CR7","volume-title":"Metalearning: Applications to Data Mining","author":"P Brazdil","year":"2008","unstructured":"Brazdil, P., et al.: Metalearning: Applications to Data Mining. Springer, Cham (2008)"},{"key":"35_CR8","volume":"40","author":"LS Buriol","year":"2020","unstructured":"Buriol, L.S., Figueiredo, C., Resende, M.G., Uchoa, E.: The guide to NP-completeness is 40 years old: an homage to David S. Johnson. Oper. Res. 40, e236329 (2020)","journal-title":"Oper. Res."},{"key":"35_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113229","volume":"148","author":"G Campuzano","year":"2020","unstructured":"Campuzano, G., Obreque, C., Aguayo, M.M.: Accelerating the Miller-Tucker-Zemlin model for the asymmetric traveling salesman problem. Expert Syst. Appl. 148, 113229 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"16","key":"35_CR10","first-page":"1152","volume":"5","author":"C Dahiya","year":"2018","unstructured":"Dahiya, C., Sangwan, S.: Literature review on travelling salesman problem. Int. J. Res. 5(16), 1152\u20131155 (2018)","journal-title":"Int. J. Res."},{"key":"35_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101334","volume":"80","author":"MA Dulebenets","year":"2023","unstructured":"Dulebenets, M.A.: A diffused memetic optimizer for reactive berth allocation and scheduling at marine container terminals in response to disruptions. Swarm Evol. Comput. 80, 101334 (2023)","journal-title":"Swarm Evol. Comput."},{"key":"35_CR12","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1038\/nature14544","volume":"521","author":"A Eiben","year":"2015","unstructured":"Eiben, A., Smith, J.: From evolutionary computation to the evolution of things. Nature 521, 476\u2013482 (2015). https:\/\/doi.org\/10.1038\/nature14544","journal-title":"Nature"},{"key":"35_CR13","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR (2017)"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Ghasemi, A., Heavey, C., Laipple, G.: A review of simulation-optimization methods with applications to semiconductor operational problems. In: 2018 Winter Simulation Conference (WSC), pp. 3672\u20133683 (2018)","DOI":"10.1109\/WSC.2018.8632486"},{"issue":"9","key":"35_CR15","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Kong, D., Su, X., Wu, S., Wang, T., Ma, P.: Detect functionally equivalent code fragments via k-nearest neighbor algorithm. In: 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), pp. 94\u201398. IEEE (2012)","DOI":"10.1109\/ICACI.2012.6463128"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Kraus, B., Matzke, S., Kirchner, E.: Utilizing a graph data structure to model physical effects and dependencies between different physical variables for the systematic identification of sensory effects in design elements. In: DS 119: Proceedings of the 33rd Symposium Design for X (DFX2022) (2022)","DOI":"10.35199\/dfx2022.09"},{"key":"35_CR18","doi-asserted-by":"publisher","unstructured":"Maarleveld, J., Guo, J., Feitosa, D.: A systematic mapping study on graph machine learning for static source code analysis. Inf. Softw. Technol. 183, 107722 (2025). https:\/\/doi.org\/10.1016\/j.infsof.2025.107722. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950584925000618","DOI":"10.1016\/j.infsof.2025.107722"},{"issue":"11","key":"35_CR19","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"35_CR20","doi-asserted-by":"publisher","unstructured":"Miranda, E.S., Fabris, F., Nascimento, C.G.M., Freitas, A.A., Oliveira, A.C.M.: Meta-learning for recommending metaheuristics for the maxsat problem. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp. 169\u2013174 (2018). https:\/\/doi.org\/10.1109\/BRACIS.2018.00037","DOI":"10.1109\/BRACIS.2018.00037"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Morais, G., Prati, R.C.: Complex network measures for data set characterization. In: Brazilian Conference on Intelligent Systems (BRACIS), pp. 12\u201318. IEEE (2013)","DOI":"10.1109\/BRACIS.2013.11"},{"key":"35_CR22","series-title":"Springer Series in Operations Research","volume-title":"Numerical Optimization","author":"J Nocedal","year":"2006","unstructured":"Nocedal, J., Wright, S.J.: Numerical Optimization. Springer Series in Operations Research, Springer, Cham (2006)"},{"key":"35_CR23","unstructured":"Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Courier Corporation (2013)"},{"key":"35_CR24","unstructured":"Pardalos, P.M., Resende, M.G.C.: Handbook of Applied Optimization. Oxford University Press (2002)"},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Putra, I.S., Rukmono, S.A., Perdana, R.S.: Abstract syntax tree (AST) and control flow graph (CFG) construction of Notasi algoritmik. In: 2021 International Conference on Data and Software Engineering (ICoDSE), pp.\u00a01\u20136 (2021)","DOI":"10.1109\/ICoDSE53690.2021.9648437"},{"key":"35_CR26","doi-asserted-by":"publisher","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987). https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7. https:\/\/www.sciencedirect.com\/science\/article\/pii\/0377042787901257","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"35_CR27","doi-asserted-by":"publisher","DOI":"10.1002\/9780470496916","volume-title":"Metaheuristics: From Design to Implementation","author":"EG Talbi","year":"2009","unstructured":"Talbi, E.G.: Metaheuristics: From Design to Implementation, vol. 74. Wiley, Hoboken (2009)"},{"issue":"6","key":"35_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3459664","volume":"54","author":"EG Talbi","year":"2021","unstructured":"Talbi, E.G.: Machine learning into metaheuristics: a survey and taxonomy. ACM Comput. Surv. 54(6), 1\u201332 (2021)","journal-title":"ACM Comput. Surv."},{"key":"35_CR29","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-weka: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 847\u2013855 (2013)","DOI":"10.1145\/2487575.2487629"},{"key":"35_CR30","volume-title":"Meta-learning: The Springer Series on Challenges in Machine Learning","author":"J Vanschoren","year":"2019","unstructured":"Vanschoren, J.: Meta-learning: The Springer Series on Challenges in Machine Learning. Springer, Cham (2019)"},{"issue":"7","key":"35_CR31","doi-asserted-by":"publisher","first-page":"4763","DOI":"10.1109\/TPAMI.2024.3357847","volume":"46","author":"A Vettoruzzo","year":"2024","unstructured":"Vettoruzzo, A., Bouguelia, M.R., Vanschoren, J., Rognvaldsson, T., Santosh, K.: Advances and challenges in meta-learning: a technical review. IEEE Trans. Pattern Anal. Mach. Intell. 46(7), 4763\u20134779 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR32","doi-asserted-by":"publisher","unstructured":"Wistuba, M., Schilling, N., Schmidt-Thieme, L.: Learning hyperparameter optimization initializations. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1\u201310 (2015). https:\/\/doi.org\/10.1109\/DSAA.2015.7344817","DOI":"10.1109\/DSAA.2015.7344817"},{"key":"35_CR33","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1613\/jair.2490","volume":"32","author":"L Xu","year":"2008","unstructured":"Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 32, 565\u2013606 (2008)","journal-title":"J. Artif. Intell. Res."},{"key":"35_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100785","volume":"60","author":"ZQ Zhang","year":"2021","unstructured":"Zhang, Z.Q., Qian, B., Hu, R., Jin, H.P., Wang, L.: A matrix-cube-based estimation of distribution algorithm for the distributed assembly permutation flow-shop scheduling problem. Swarm Evol. Comput. 60, 100785 (2021)","journal-title":"Swarm Evol. Comput."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05176-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:41:35Z","timestamp":1757943695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05176-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"ISBN":["9783032051752","9783032051769"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05176-9_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"15 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2025.ualg.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}