{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:20:14Z","timestamp":1743013214875,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031700675"},{"type":"electronic","value":"9783031700682"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70068-2_10","type":"book-chapter","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T19:02:54Z","timestamp":1725649374000},"page":"154-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybridizing Target- and\u00a0SHAP-Encoded Features for\u00a0Algorithm Selection in\u00a0Mixed-Variable Black-Box Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5383-7475","authenticated-orcid":false,"given":"Konstantin","family":"Dietrich","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1237-4248","authenticated-orcid":false,"given":"Raphael Patrick","family":"Prager","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-3227","authenticated-orcid":false,"given":"Carola","family":"Doerr","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9788-8282","authenticated-orcid":false,"given":"Heike","family":"Trautmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,7]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)","DOI":"10.1145\/3292500.3330701"},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1007\/978-3-319-67988-4_140","volume-title":"Advances in Structural and Multidisciplinary Optimization","author":"PJ Barjhoux","year":"2018","unstructured":"Barjhoux, P.J., Diouane, Y., Grihon, S., Bettebghor, D., Morlier, J.: Mixed variable structural optimization: toward an efficient hybrid algorithm. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, K.U., Maute, K. (eds.) Advances in Structural and Multidisciplinary Optimization, pp. 1880\u20131896. Springer International Publishing, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-67988-4_140"},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Belkhir, N., Dr\u00e9o, J., Sav\u00e9ant, P., Schoenauer, M.: Per instance algorithm configuration of CMA-ES with limited budget. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 681\u2013688. ACM (2017). https:\/\/doi.org\/10.1145\/3071178.3071343","DOI":"10.1145\/3071178.3071343"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"89497","DOI":"10.1109\/ACCESS.2020.2990567","volume":"8","author":"J Blank","year":"2020","unstructured":"Blank, J., Deb, K.: pymoo: multi-objective optimization in Python. IEEE Access 8, 89497\u201389509 (2020)","journal-title":"IEEE Access"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Guzowski, H., Smolka, M.: Configuring a hierarchical evolutionary strategy using exploratory landscape analysis. In: Silva, S., Paquete, L. (eds.) Proceedings of Genetic and Evolutionary Computation Conference (GECCO), Companion, pp. 1785\u20131792. ACM (2023). https:\/\/doi.org\/10.1145\/3583133.3596403","DOI":"10.1145\/3583133.3596403"},{"key":"10_CR6","unstructured":"Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking 2010: experimental setup. Research Report RR-7215, INRIA (Mar 2010). https:\/\/inria.hal.science\/inria-00462481"},{"issue":"1","key":"10_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1162\/evco_a_00242","volume":"27","author":"P Kerschke","year":"2019","unstructured":"Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27(1), 3\u201345 (2019). https:\/\/doi.org\/10.1162\/evco_a_00242","journal-title":"Evol. Comput."},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"Kerschke, P., Preuss, M., Wessing, S., Trautmann, H.: detecting funnel structures by means of exploratory landscape analysis. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 265\u2013272. GECCO \u201915, Association for Computing Machinery, New York, NY, USA (2015). https:\/\/doi.org\/10.1145\/2739480.2754642","DOI":"10.1145\/2739480.2754642"},{"issue":"1","key":"10_CR9","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/evco_a_00236","volume":"27","author":"P Kerschke","year":"2019","unstructured":"Kerschke, P., Trautmann, H.: Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning. Evol. Comput. 27(1), 99\u2013127 (2019). https:\/\/doi.org\/10.1162\/evco_a_00236","journal-title":"Evol. Comput."},{"key":"10_CR10","series-title":"Studies in Classification, Data Analysis, and Knowledge Organization","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-3-030-25147-5_7","volume-title":"Applications in Statistical Computing","author":"P Kerschke","year":"2019","unstructured":"Kerschke, P., Trautmann, H.: Comprehensive feature-based landscape analysis of continuous and constrained optimization problems using the R-package flacco. In: Bauer, N., Ickstadt, K., L\u00fcbke, K., Szepannek, G., Trautmann, H., Vichi, M. (eds.) Applications in Statistical Computing. SCDAKO, pp. 93\u2013123. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-25147-5_7"},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/978-3-031-14714-2_4","volume-title":"Parallel Problem Solving from Nature - PPSN XVII","author":"D Vermetten","year":"2022","unstructured":"Vermetten, D., et al.: Per-run algorithm selection with warm-starting using trajectory-based features. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tu\u0161ar, T. (eds.) Parallel Problem Solving from Nature - PPSN XVII, pp. 46\u201360. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-14714-2_4"},{"key":"10_CR12","unstructured":"Lindauer, M., et al.: SMAC3: a versatile bayesian optimization package for hyperparameter optimization. J. Mach. Learn. Res. 23, 1\u20139 (2022). https:\/\/www.jmlr.org\/papers\/volume23\/21-0888\/21-0888.pdf"},{"issue":"3","key":"10_CR13","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s10287-005-0024-2","volume":"2","author":"G Liuzzi","year":"2005","unstructured":"Liuzzi, G., Lucidi, S., Piccialli, V., Villani, M.: Design of induction motors using a mixed-variable approach. Comput. Manage. Sci. 2(3), 213\u2013228 (2005). https:\/\/doi.org\/10.1007\/s10287-005-0024-2","journal-title":"Comput. Manage. Sci."},{"issue":"4","key":"10_CR14","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1137\/S1052623403429573","volume":"15","author":"S Lucidi","year":"2005","unstructured":"Lucidi, S., Piccialli, V., Sciandrone, M.: An algorithm model for mixed variable programming. SIAM J. Optim. 15(4), 1057\u20131084 (2005). https:\/\/doi.org\/10.1137\/S1052623403429573","journal-title":"SIAM J. Optim."},{"key":"10_CR15","doi-asserted-by":"publisher","unstructured":"Lunacek, M., Whitley, D.: The dispersion metric and the CMA evolution strategy. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 477\u2013484. GECCO 2006, Association for Computing Machinery, New York, NY, USA (2006). https:\/\/doi.org\/10.1145\/1143997.1144085","DOI":"10.1145\/1143997.1144085"},{"key":"10_CR16","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765\u20134774. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., Rudolph, G.: Exploratory landscape analysis. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 829\u2013836. GECCO 2011, Association for Computing Machinery, New York, NY, USA (2011). https:\/\/doi.org\/10.1145\/2001576.2001690","DOI":"10.1145\/2001576.2001690"},{"issue":"1","key":"10_CR18","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/507533.507538","volume":"3","author":"D Micci-Barreca","year":"2001","unstructured":"Micci-Barreca, D.: A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. SIGKDD Explor. Newsl. 3(1), 27\u201332 (2001). https:\/\/doi.org\/10.1145\/507533.507538","journal-title":"SIGKDD Explor. Newsl."},{"issue":"1","key":"10_CR19","first-page":"1","volume":"23","author":"R Mitchell","year":"2022","unstructured":"Mitchell, R., Cooper, J., Frank, E., Holmes, G.: Sampling permutations for shapley value estimation. J. Mach. Learn. Res. 23(1), 1\u201346 (2022)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10_CR20","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TEVC.2014.2302006","volume":"19","author":"MA Mu\u00f1oz Acosta","year":"2015","unstructured":"Mu\u00f1oz Acosta, M.A., Kirley, M., Halgamuge, S.K.: Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Trans. Evol. Comput. (TEVC) 19(1), 74\u201387 (2015). https:\/\/doi.org\/10.1109\/TEVC.2014.2302006","journal-title":"IEEE Trans. Evol. Comput. (TEVC)"},{"key":"10_CR21","doi-asserted-by":"publisher","unstructured":"Mu\u00f1oz, M.A., Sun, Y., Kirley, M., Halgamuge, S.K.: Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges. Inf. Sci. 317, 224\u2013245 (2015). https:\/\/doi.org\/10.1016\/j.ins.2015.05.010, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025515003680","DOI":"10.1016\/j.ins.2015.05.010"},{"key":"10_CR22","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR23","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-319-67988-4_5","volume-title":"Advances in Structural and Multidisciplinary Optimization","author":"J Pelamatti","year":"2018","unstructured":"Pelamatti, J., Brevault, L., Balesdent, M., Talbi, E.G., Guerin, Y.: How to deal with mixed-variable optimization problems: an overview of algorithms and formulations. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, K.U., Maute, K. (eds.) Advances in Structural and Multidisciplinary Optimization, pp. 64\u201382. Springer International Publishing, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-67988-4_5"},{"key":"10_CR24","unstructured":"Pfisterer, F., Schneider, L., Moosbauer, J., Binder, M., Bischl, B.: YAHPO Gym - an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization. In: Guyon, I., Lindauer, M., van\u00a0der Schaar, M., Hutter, F., Garnett, R. (eds.) Proceedings of the First International Conference on Automated Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0188, pp. 3\/1\u201339. PMLR (2022). https:\/\/proceedings.mlr.press\/v188\/pfisterer22a.html"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"Pikalov, M., Mironovich, V.: Automated parameter choice with exploratory landscape analysis and machine learning. In: Krawiec, K. (ed.) Proceedings of Genetic and Evolutionary Computation Conference (GECCO), Companion, pp. 1982\u20131985. ACM (2021). https:\/\/doi.org\/10.1145\/3449726.3463213","DOI":"10.1145\/3449726.3463213"},{"key":"10_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1007\/978-3-031-02462-7_44","volume-title":"Applications of Evolutionary Computation","author":"M Pikalov","year":"2022","unstructured":"Pikalov, M., Mironovich, V.: Parameter tuning for the $${(1 + (\\lambda , \\lambda ))}$$ genetic algorithm using landscape analysis and machine learning. In: Jim\u00e9nez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds.) EvoApplications 2022. LNCS, vol. 13224, pp. 704\u2013720. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-02462-7_44"},{"key":"10_CR27","doi-asserted-by":"publisher","unstructured":"Prager, R.P., Trautmann, H.: Investigating the viability of existing exploratory landscape analysis features for mixed-integer problems. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 451\u2013454. GECCO 2023 Companion, Association for Computing Machinery, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3583133.3590757","DOI":"10.1145\/3583133.3590757"},{"key":"10_CR28","volume-title":"Appl. Evol. Comput.","author":"RP Prager","year":"2023","unstructured":"Prager, R.P., Trautmann, H.: Nullifying the inherent bias of non-invariant exploratory landscape analysis features. In: Correia, J., Smith, S., Qaddoura, R. (eds.) Appl. Evol. Comput. Springer International Publishing, Cham (2023)"},{"key":"10_CR29","doi-asserted-by":"publisher","unstructured":"Prager, R.P., Trautmann, H.: Pflacco: Feature-based landscape analysis of continuous and constrained optimization problems in Python. Evol. Comput., 1\u201325 (2023). https:\/\/doi.org\/10.1162\/evco_a_00341","DOI":"10.1162\/evco_a_00341"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Prager, R.P., Trautmann, H.: Exploratory landscape analysis for mixed-variable problems. CoRR arXiv preprint arXiv:2402.16467 (2024). https:\/\/arxiv.org\/abs\/2402.16467, under revision with IEEE Transactions on Evolutionary Computation","DOI":"10.1109\/TEVC.2024.3399560"},{"key":"10_CR31","doi-asserted-by":"publisher","unstructured":"Seiler, M.V., Prager, R.P., Kerschke, P., Trautmann, H.: A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 657\u2013665. Association for Computing Machinery, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3512290.3528834","DOI":"10.1145\/3512290.3528834"},{"key":"10_CR32","first-page":"307","volume-title":"Contributions to the Theory of Games II","author":"LS Shapley","year":"1953","unstructured":"Shapley, L.S.: A value for n-person games. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games II, pp. 307\u2013317. Princeton University Press, Princeton (1953)"},{"key":"10_CR33","doi-asserted-by":"publisher","unstructured":"Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1) (2009). https:\/\/doi.org\/10.1145\/1456650.1456656","DOI":"10.1145\/1456650.1456656"},{"issue":"2","key":"10_CR34","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2013","unstructured":"Vanschoren, J., van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. SIGKDD Explor. 15(2), 49\u201360 (2013). https:\/\/doi.org\/10.1145\/2641190.2641198","journal-title":"SIGKDD Explor."},{"issue":"1","key":"10_CR35","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s00158-003-0318-3","volume":"26","author":"G Venter","year":"2003","unstructured":"Venter, G., Sobieszczanski-Sobieski, J.: Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization. Struct. Multidiscip. Optim. 26(1), 121\u2013131 (2003). https:\/\/doi.org\/10.1007\/s00158-003-0318-3","journal-title":"Struct. Multidiscip. Optim."}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVIII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70068-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T19:07:19Z","timestamp":1725649639000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70068-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031700675","9783031700682"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70068-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"7 September 2024","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":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hagenberg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2024.fh-ooe.at\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}