{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T10:00:06Z","timestamp":1756461606982,"version":"3.40.5"},"reference-count":69,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T00:00:00Z","timestamp":1608163200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["309935\/2017\u20102","439226\/2018\u20100"],"award-info":[{"award-number":["309935\/2017\u20102","439226\/2018\u20100"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int Trans Operational Res"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The Algorithm Selection Problem (ASP) considers the use of previous knowledge regarding problem features and algorithm performance to recommend the best strategy to solve a previously unseen problem. In the application context, the usual ASP for optimization considers recommending the best heuristics, whenever it faces a new similar problem instance, also known as the Per\u2010Instance ASP. Although ASP for heuristic recommendation is not new, selecting heuristics and also their parameters, or the Per\u2010instance Algorithm Configuration Problem, is still considered a challenging task. This paper investigates the use of meta\u2010learning to recommend six different stochastic local searches and their parameters to solve several instances of permutation flowshop problems. The proposed approach uses several problem features, including fitness landscape metrics, builds the performance database using<jats:italic>irace<\/jats:italic>, and trains different multi\u2010label recommendation models on a data set with more than 6000 flowshop problem instances. Experiments show that decision tree\u2010based machine learning models achieve good performance, and the quality of the recommendations is capable of outperforming the state\u2010of\u2010the\u2010art algorithm with tuned\u00a0configuration.<\/jats:p>","DOI":"10.1111\/itor.12922","type":"journal-article","created":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T10:32:18Z","timestamp":1608287538000},"page":"774-799","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Stochastic local search and parameters recommendation: a case study on flowshop problems"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5622-392X","authenticated-orcid":false,"given":"Lucas M.","family":"Pavelski","sequence":"first","affiliation":[{"name":"CPGEI\/Federal University of Technology \u2010 Paran\u00e1 Av. Sete de Setembro Curitiba Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2791-174X","authenticated-orcid":false,"given":"Myriam","family":"Delgado","sequence":"additional","affiliation":[{"name":"CPGEI\/Federal University of Technology \u2010 Paran\u00e1 Av. Sete de Setembro Curitiba Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4372-5162","authenticated-orcid":false,"given":"Marie\u2010\u00c9l\u00e9onore","family":"Kessaci","sequence":"additional","affiliation":[{"name":"Univ. Lille CNRS, Centrale Lille, UMR 9189 CRIStAL, F\u201059000 Lille France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9825-4700","authenticated-orcid":false,"given":"Alex A.","family":"Freitas","sequence":"additional","affiliation":[{"name":"School of Computing University of Kent Canterbury UK"}]}],"member":"311","published-online":{"date-parts":[[2020,12,17]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"Bischl B. Mersmann O. Trautmann H. Preu\u00df M. 2012. Algorithm selection based on exploratory landscape analysis and cost\u2010sensitive learning. Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation.ACM New York NY pp.313\u2013320.","DOI":"10.1145\/2330163.2330209"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"journal-title":"Classification and Regression Trees","year":"1984","author":"Breiman L.","key":"e_1_2_8_4_1"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2020.105044"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91086-4_14"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"Chen T. Guestrin C. 2016. Xgboost: A scalable tree boosting system. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM New York NY pp.785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"volume-title":"Practical Nonparametric Statistics","year":"1999","author":"Conover W.","key":"e_1_2_8_8_1"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-010-9155-x"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"Dantas A.L. Pozo A.T.R. 2018. A meta\u2010learning algorithm selection approach for the quadratic assignment problem. 2018 IEEE Congress on Evolutionary Computation (CEC) July 8\u201313 Rio de Janeiro Brazil pp.1\u20138.","DOI":"10.1109\/CEC.2018.8477989"},{"key":"e_1_2_8_11_1","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar J.","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2016.12.021"},{"key":"e_1_2_8_13_1","doi-asserted-by":"crossref","unstructured":"Feurer M. Springenberg J.T. Hutter F. 2015. Initializing Bayesian hyperparameter optimization via meta\u2010learning. Proceedings of the Twenty\u2010Ninth AAAI Conference on Artificial Intelligence January 25\u201330 Austin TX pp.1128\u20131135.","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.1.2.117"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1.3.190"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.10.036"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/itor.12906"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"Hernando L. Daolio F. Veerapen N. Ochoa G. 2017. Local optima networks of the permutation flowshop scheduling problem: makespan vs. total flow time. 2017 IEEE Congress on Evolutionary Computation (CEC).IEEE Piscataway NJ pp.1964\u20131971.","DOI":"10.1109\/CEC.2017.7969541"},{"journal-title":"Stochastic Local Search: Foundations and Applications","year":"2004","author":"Hoos H.H.","key":"e_1_2_8_20_1"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-43505-2_54"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/nav.3800010110"},{"key":"e_1_2_8_23_1","unstructured":"Jones T. 1995. Evolutionary algorithms fitness landscapes and search. PhD thesis University of New Mexico Albuquerque NM."},{"key":"e_1_2_8_24_1","unstructured":"Kadioglu S. Malitsky Y. Sellmann M. Tierney K. 2010. ISAC\u2013instance\u2010specific algorithm configuration. Proceedings of the 2010 Conference on ECAI 2010: 19th European Conference on Artificial Intelligence.IOS Press Amsterdam The Netherlands pp.751\u2013756."},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.3233\/HIS-2011-0133"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.04.027"},{"key":"e_1_2_8_27_1","doi-asserted-by":"crossref","unstructured":"Kanda J. Soares C. Hruschka E. De Carvalho A. 2012. A meta\u2010learning approach to select meta\u2010heuristics for the traveling salesman problem using MLP\u2010Based label ranking. International Conference on Neural Information Processing.Springer Doha Qatar pp.488\u2013495.","DOI":"10.1007\/978-3-642-34487-9_59"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00242"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00215"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.06.039"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-013-9406-y"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.orp.2016.09.002"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.03.004"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20364-0_17"},{"key":"e_1_2_8_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25566-3_18"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38516-2_12"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-19084-6_14"},{"key":"e_1_2_8_39_1","first-page":"30","volume-title":"Hybrid Metaheuristics","author":"Mascia F.","year":"2014"},{"key":"e_1_2_8_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/0305-0483(83)90088-9"},{"key":"e_1_2_8_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/0305-0483(89)90059-5"},{"key":"e_1_2_8_42_1","doi-asserted-by":"publisher","DOI":"10.1111\/itor.12902"},{"key":"e_1_2_8_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-013-9186-9"},{"key":"e_1_2_8_44_1","doi-asserted-by":"crossref","unstructured":"Pavelski L. Delgado M. Kessaci M. 2018a. Meta\u2010learning for optimization: a case study on the flowshop problem using decision trees. 2018 IEEE Congress on Evolutionary Computation (CEC) July 8\u201313 Rio de Janeiro Brazil pp.1\u20138.","DOI":"10.1109\/CEC.2018.8477664"},{"key":"e_1_2_8_45_1","doi-asserted-by":"crossref","unstructured":"Pavelski L.M. Delgado M.R. Kessaci M.E. 2019. Meta\u2010learning on flowshop using fitness landscape analysis. Proceedings of the Genetic and Evolutionary Computation Conference.ACM New York NY pp.925\u2013933.","DOI":"10.1145\/3321707.3321846"},{"key":"e_1_2_8_46_1","doi-asserted-by":"crossref","unstructured":"Pavelski L.M. Kessaci M. Delgado M.R. 2018b. Recommending meta\u2010heuristics and configurations for the flowshop problem via meta\u2010learning: analysis and design. 2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Sao Paulo Brazil pp.163\u2013168.","DOI":"10.1109\/BRACIS.2018.00036"},{"key":"e_1_2_8_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26580-3"},{"key":"e_1_2_8_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23229-9_8"},{"key":"e_1_2_8_49_1","doi-asserted-by":"crossref","unstructured":"Pohlert T. 2019. PMCMRplus: calculate pairwise multiple comparisons of mean rank sums extended. R package v1.4.2 (accessed 21 October 2019).","DOI":"10.32614\/CRAN.package.PMCMRplus"},{"key":"e_1_2_8_50_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018983524911"},{"key":"e_1_2_8_51_1","first-page":"65","volume-title":"Advances in Computers","author":"Rice J.R.","year":"1976"},{"key":"e_1_2_8_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2004.04.017"},{"key":"e_1_2_8_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.12.009"},{"volume-title":"Artificial Intelligence: A Modern Approach","year":"2003","author":"Russell S.J.","key":"e_1_2_8_54_1"},{"key":"e_1_2_8_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2011.07.006"},{"key":"e_1_2_8_56_1","doi-asserted-by":"crossref","unstructured":"Smith\u2010Miles K.A. 2008. Towards insightful algorithm selection for optimisation using meta\u2010learning concepts. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).IEEE Hong Kong pp.4118\u20134124.","DOI":"10.1109\/IJCNN.2008.4634391"},{"key":"e_1_2_8_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/1456650.1456656"},{"key":"e_1_2_8_58_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-11169-3_7"},{"key":"e_1_2_8_59_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-30671-6_16"},{"key":"e_1_2_8_60_1","unstructured":"St\u00fctzle T. 1997. An ant approach to the flow shop problem. Proceedings of the 6th European Congress on Intelligent Techniques & Soft Computing (EUFIT'98) September 7\u201310 Aachen Germany pp.1560\u20131564."},{"key":"e_1_2_8_61_1","unstructured":"St\u00fctzle T. 1998. Applying iterated local search to the permutation flow shop problem. Technical report FG Intellektik TU Darmstadt Germany."},{"key":"e_1_2_8_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-2217(90)90090-X"},{"key":"e_1_2_8_63_1","doi-asserted-by":"publisher","DOI":"10.1162\/106365600568095"},{"key":"e_1_2_8_64_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-017-9328-y"},{"key":"e_1_2_8_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-1665-5_20"},{"key":"e_1_2_8_66_1","unstructured":"Watson J.P. Barbulescu L. Howe A.E. Whitley L.D. 1999. Algorithm performance and problem structure for flow\u2010shop scheduling.AAAI\/IAAI. American Association for Artificial Intelligence Menlo Park CA pp.688\u2013695."},{"key":"e_1_2_8_67_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00202749"},{"key":"e_1_2_8_68_1","unstructured":"Wu X.Z. Zhou Z.H. 2017. A unified view of multi\u2010label performance measures. Proceedings of the 34th International Conference on Machine Learning pp.3780\u20133788."},{"key":"e_1_2_8_69_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2490"},{"key":"e_1_2_8_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.39"}],"container-title":["International Transactions in Operational Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/itor.12922","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/itor.12922","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/itor.12922","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T13:43:12Z","timestamp":1724074992000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/itor.12922"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,17]]},"references-count":69,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["10.1111\/itor.12922"],"URL":"https:\/\/doi.org\/10.1111\/itor.12922","archive":["Portico"],"relation":{},"ISSN":["0969-6016","1475-3995"],"issn-type":[{"type":"print","value":"0969-6016"},{"type":"electronic","value":"1475-3995"}],"subject":[],"published":{"date-parts":[[2020,12,17]]},"assertion":[{"value":"2020-01-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-30","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}