{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T14:33:37Z","timestamp":1759847617957,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031147135"},{"type":"electronic","value":"9783031147142"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-14714-2_1","type":"book-chapter","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T21:03:13Z","timestamp":1660424593000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automated Algorithm Selection in\u00a0Single-Objective Continuous Optimization: A Comparative Study of\u00a0Deep Learning and\u00a0Landscape Analysis Methods"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1237-4248","authenticated-orcid":false,"given":"Raphael Patrick","family":"Prager","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1750-9060","authenticated-orcid":false,"given":"Moritz Vinzent","family":"Seiler","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9788-8282","authenticated-orcid":false,"given":"Heike","family":"Trautmann","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2862-1418","authenticated-orcid":false,"given":"Pascal","family":"Kerschke","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/3-540-44503-X_27","volume-title":"Database Theory \u2014 ICDT 2001","author":"CC Aggarwal","year":"2001","unstructured":"Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420\u2013434. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44503-X_27"},{"doi-asserted-by":"crossref","unstructured":"Alissa, M., Sim, K., Hart, E.: Algorithm selection using deep learning without feature extraction. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 198\u2013206 (2019)","key":"1_CR2","DOI":"10.1145\/3321707.3321845"},{"doi-asserted-by":"crossref","unstructured":"Bischl, B., Mersmann, O., Trautmann, H., Preu\u00df, M.: Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 313\u2013320 (2012)","key":"1_CR3","DOI":"10.1145\/2330163.2330209"},{"doi-asserted-by":"crossref","unstructured":"Bossek, J., Doerr, C., Kerschke, P.: Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB. In: Proceedings of the 22nd Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 778\u2013786 (2020)","key":"1_CR4","DOI":"10.1145\/3377930.3390155"},{"unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http:\/\/www.deeplearningbook.org","key":"1_CR5"},{"issue":"2","key":"1_CR6","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"MH Guo","year":"2021","unstructured":"Guo, M.H., Cai, J.X., Liu, Z.N., Mu, T.J., Martin, R.R., Hu, S.M.: PCT: point cloud transformer. Comput. Visual Media 7(2), 187\u2013199 (2021)","journal-title":"Comput. Visual Media"},{"unstructured":"Hansen, N., Auger, A., Finck, S., Ros, R.: Real-Parameter Black-Box Optimization Benchmarking 2010: Experimental Setup. Research Report RR-7215, INRIA (2010). https:\/\/hal.inria.fr\/inria-00462481","key":"1_CR7"},{"issue":"1","key":"1_CR8","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1080\/10556788.2020.1808977","volume":"36","author":"N Hansen","year":"2021","unstructured":"Hansen, N., Auger, A., Ros, R., Mersmann, O., Tu\u0161ar, T., Brockhoff, D.: COCO: a platform for comparing continuous optimizers in a black-box setting. Optimi. Meth. Software 36(1), 114\u2013144 (2021)","journal-title":"Optimi. Meth. Software"},{"unstructured":"Hansen, N., Finck, S., Ros, R., Auger, A.: Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. Technical report RR-6829, INRIA (2009). https:\/\/hal.inria.fr\/inria-00362633\/document","key":"1_CR9"},{"unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)","key":"1_CR10"},{"unstructured":"Jones, T., Forrest, S.: Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the 6th International Conference on Genetic Algorithms (ICGA), pp. 184\u2013192. Morgan Kaufmann Publishers Inc. (1995)","key":"1_CR11"},{"issue":"1","key":"1_CR12","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. (ECJ) 27(1), 3\u201345 (2019)","journal-title":"Evol. Comput. (ECJ)"},{"doi-asserted-by":"crossref","unstructured":"Kerschke, P., Preuss, M., Wessing, S., Trautmann, H.: Detecting funnel structures by means of exploratory landscape analysis. In: Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 265\u2013272. ACM, July 2015","key":"1_CR13","DOI":"10.1145\/2739480.2754642"},{"issue":"1","key":"1_CR14","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. (ECJ) 27(1), 99\u2013127 (2019)","journal-title":"Evol. Comput. (ECJ)"},{"key":"1_CR15","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"},{"issue":"10","key":"1_CR16","first-page":"1995","volume":"3361","author":"Y LeCun","year":"1995","unstructured":"LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Networks 3361(10), 1995 (1995)","journal-title":"Handbook Brain Theory Neural Networks"},{"doi-asserted-by":"crossref","unstructured":"Loshchilov, I., Schoenauer, M., S\u00e8bag, M.: Bi-population CMA-ES algorithms with surrogate models and line searches. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation. GECCO 2013 Companion, pp. 1177\u20131184. ACM (2013)","key":"1_CR17","DOI":"10.1145\/2464576.2482696"},{"doi-asserted-by":"crossref","unstructured":"Lunacek, M., Whitley, L.D.: The dispersion metric and the CMA evolution strategy. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 477\u2013484. ACM (2006)","key":"1_CR18","DOI":"10.1145\/1143997.1144085"},{"key":"1_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Ma","year":"2018","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8"},{"key":"1_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.ins.2013.04.015","volume":"241","author":"KM Malan","year":"2013","unstructured":"Malan, K.M., Engelbrecht, A.P.: A survey of techniques for characterising fitness landscapes and some possible ways forward. Inf. Sci. (JIS) 241, 148\u2013163 (2013)","journal-title":"Inf. Sci. (JIS)"},{"doi-asserted-by":"crossref","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 (GECCO), pp. 829\u2013836. ACM (2011). Recipient of the 2021 ACM SigEVO Impact Award","key":"1_CR21","DOI":"10.1145\/2001576.2001690"},{"issue":"1","key":"1_CR22","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)","journal-title":"IEEE Trans. Evol. Comput. (TEVC)"},{"key":"1_CR23","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.ins.2015.05.010","volume":"317","author":"MA Mu\u00f1oz Acosta","year":"2015","unstructured":"Mu\u00f1oz Acosta, 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. (JIS) 317, 224\u2013245 (2015)","journal-title":"Inf. Sci. (JIS)"},{"issue":"1","key":"1_CR24","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3390\/a14010019","volume":"14","author":"MA Mu\u00f1oz","year":"2021","unstructured":"Mu\u00f1oz, M.A., Kirley, M.: Sampling effects on algorithm selection for continuous black-box optimization. Algorithms 14(1), 19 (2021). https:\/\/doi.org\/10.3390\/a14010019","journal-title":"Algorithms"},{"unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML 2010, Madison, WI, USA, pp. 807\u2013814. Omnipress (2010)","key":"1_CR25"},{"doi-asserted-by":"crossref","unstructured":"Pearson, K.: On lines and planes of closest fit to system of points in space. Philos. Mug 6th ser. 2, 559\u2013572 (1901)","key":"1_CR26","DOI":"10.1080\/14786440109462720"},{"key":"1_CR27","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."},{"doi-asserted-by":"crossref","unstructured":"Prager, R.P., Seiler, M.V., Trautmann, H., Kerschke, P.: Towards feature-free automated algorithm selection for single-objective continuous black-box optimization. In: Proceedings of the IEEE Symposium Series on Computational Intelligence. Orlando, Florida, USA (2021)","key":"1_CR28","DOI":"10.1109\/SSCI50451.2021.9660174"},{"doi-asserted-by":"crossref","unstructured":"Prager, R.P., Trautmann, H., Wang, H., B\u00e4ck, T.H.W., Kerschke, P.: Per-instance configuration of the modularized CMA-ES by means of classifier chains and exploratory landscape analysis. In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), pp. 996\u20131003. IEEE (2020)","key":"1_CR29","DOI":"10.1109\/SSCI47803.2020.9308510"},{"issue":"24","key":"1_CR30","doi-asserted-by":"publisher","first-page":"638","DOI":"10.21105\/joss.00638","volume":"3","author":"S Raschka","year":"2018","unstructured":"Raschka, S.: MLxtend: providing machine learning and data science utilities and extensions to python\u2019s scientific computing stack. J. Open Source Software (JOSS) 3(24), 638 (2018)","journal-title":"J. Open Source Software (JOSS)"},{"issue":"65\u2013118","key":"1_CR31","first-page":"5","volume":"15","author":"JR Rice","year":"1976","unstructured":"Rice, J.R.: The algorithm selection problem. Adv. Comput. 15(65\u2013118), 5 (1976)","journal-title":"Adv. Comput."},{"key":"1_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-030-58112-1_4","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"M Seiler","year":"2020","unstructured":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., Trautmann, H.: Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem. In: B\u00e4ck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12269, pp. 48\u201364. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58112-1_4"},{"doi-asserted-by":"crossref","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. arXiv preprint (2022)","key":"1_CR33","DOI":"10.1145\/3512290.3528834"},{"doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-Attention with Relative Position Representations. arXiv preprint arXiv:1803.02155 (2018)","key":"1_CR34","DOI":"10.18653\/v1\/N18-2074"},{"unstructured":"Turney, P.D.: Types of Cost in Inductive Concept Learning. arXiv preprint cs\/0212034 (2002)","key":"1_CR35"},{"unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)","key":"1_CR36"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14714-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:40:16Z","timestamp":1710261616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14714-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031147135","9783031147142"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14714-2_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Dortmund","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2022.cs.tu-dortmund.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"185","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.75","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.11","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}