{"type":"ScholarlyArticle","title":"A Survey of Methods for Automated Algorithm Configuration (Extended Abstract)","authors":[{"name":"Elias Schede","slug":"elias-schede","given":"Elias","family":"Schede","affiliation":"Decision and Operation Technologies Group , Bielefeld University , Bielefeld , Germany"},{"name":"Jasmin Brandt","slug":"jasmin-brandt","given":"Jasmin","family":"Brandt","affiliation":"Department of Computer Science , Paderborn University , Paderborn , Germany"},{"name":"Alexander Tornede","slug":"alexander-tornede","given":"Alexander","family":"Tornede","affiliation":"Institute of Artificial Intelligence , Leibniz University Hannover , Hannover , Germany"},{"name":"Marcel Wever","slug":"marcel-wever","given":"Marcel","family":"Wever","affiliation":"Institute of Informatics , LMU Munich , Munich , Germany; Munich Center for Machine Learning , Munich , Germany"},{"name":"Viktor Bengs","slug":"viktor-bengs","given":"Viktor","family":"Bengs","affiliation":"Institute of Informatics , LMU Munich , Munich , Germany; Munich Center for Machine Learning , Munich , Germany"},{"name":"Eyke Hüllermeier","slug":"eyke-hullermeier","given":"Eyke","family":"Hüllermeier","affiliation":""},{"name":"Kevin Tierney","slug":"kevin-tierney","given":"Kevin","family":"Tierney","affiliation":"Decision and Operation Technologies Group , Bielefeld University , Bielefeld , Germany"}],"abstract":"Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There are currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. Existing AC literature is classified and characterized by the provided taxonomies.","doi":"10.24963/ijcai.2023/791","url":"https://ijcai.org/proceedings/2023/791","pdf":"https://ijcai.org/proceedings/2023/0791.pdf","markdown":"https://ijcai.org/proceedings/2023/791.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/791","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Journal Track","track":"Journal Track","number":791,"pages":{"first":6964,"last":6968},"keywords":["Search: S: Algorithm portfolios and configuration"],"videos":[],"references":[{"position":1,"raw":"[Ansótegui et al., 2021] Carlos Ansótegui, Josep Pon, Meinolf Sellmann, and Kevin Tierney. PyDGGA: Dis- tributed GGA for automatic configuration. In Theory and Applications of Satisfiability Testing -SAT, volume 12831 of Lecture Notes in Computer Science, pages 11-20. Springer, 2021.","title":"PyDGGA: Distributed GGA for automatic configuration","authors":"Carlos Ansótegui, Josep Pon, Meinolf Sellmann, Kevin Tierney","year":"2021","venue":"Theory and Applications of Satisfiability Testing -SAT","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Audemard and Simon, 2009] Gilles Audemard and Laurent Simon. Predicting learnt clauses quality in modern SAT solvers. In Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI, pages 399- 404, 2009.","title":"Predicting learnt clauses quality in modern SAT solvers","authors":"Simon ; Audemard, Gilles Audemard, Laurent Simon","year":"2009","venue":"Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2009/74"},{"position":3,"raw":"[Balint and Manthey, 2013] Adrian Balint and Norbert Man- they. Sparrowtoriss.s. Proceedings of SAT Competition 2014, pages 87-88, 2013.","title":"Adrian Balint and Norbert Manthey. Sparrowtoriss.s","authors":"Manthey Balint","year":"2013","venue":"Proceedings of SAT Competition 2014","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bischl et al., 2021] Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, and Marius Lindauer. Hyperpa- rameter optimization: Foundations, algorithms, best prac- tices and open challenges. CoRR, abs/2107.05847, 2021.","title":"Hyperparameter optimization: Foundations, algorithms, best practices and open challenges","authors":"Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Eiben and Smit, 2011a] Agoston E. Eiben and Selmar K. Smit. Evolutionary algorithm parameters and methods to tune them. In Autonomous search, pages 15-36. Springer, 2011.","title":"Evolutionary algorithm parameters and methods to tune them","authors":"Eiben, E Smit ; A] Agoston, Selmar K Eiben, Smit","year":"2011","venue":"Autonomous search","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Eiben and Smit, 2011b] Agoston E. Eiben and Selmar K. Smit. Parameter tuning for configuring and analyzing evo- lutionary algorithms. Swarm and Evolutionary Computa- tion, 1(1):19-31, 2011.","title":"Parameter tuning for configuring and analyzing evolutionary algorithms","authors":"Eiben, E Smit ; B] Agoston, Selmar K Eiben, Smit","year":"2011","venue":"Swarm and Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Elshawi et al., 2019] Radwa Elshawi, Mohamed Maher, and Sherif Sakr. Automated machine learning: State-of-the-art and open challenges. arXiv preprint arXiv:1906.02287, 2019.","title":"Automated machine learning: State-of-the-art and open challenges","authors":"Radwa Elshawi, Mohamed Maher, Sherif Sakr","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Eryoldas ¸and Durmus ¸oglu, 2021] Yasemin Eryoldas ¸and Alptekin Durmus ¸oglu. A literature survey on instance spe- cific algorithm configuration methods. In Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management, pages 2983- 2990. IEOM Society International, 2021.","title":"Yasemin Eryoldas ¸and Alptekin Durmus ¸oglu. A literature survey on instance specific algorithm configuration methods","authors":"Durmus Eryoldas, ¸oglu","year":"2021","venue":"Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2019] Changwu Huang, Yuanxiang Li, and Xin Yao. A survey of automatic parameter tuning meth- ods for metaheuristics. IEEE transactions on evolutionary computation, 24(2):201-216, 2019.","title":"A survey of automatic parameter tuning methods for metaheuristics","authors":"Huang","year":"2019","venue":"IEEE transactions on evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hutter et al., 2007a] Frank Hutter, Domagoj Babic, Hol- ger H. Hoos, and Alan J. Hu. Boosting verification by au- tomatic tuning of decision procedures. In Formal Methods in Computer Aided Design, FMCAD, pages 27-34. IEEE, 2007.","title":"Boosting verification by automatic tuning of decision procedures","authors":"Hutter","year":"2007","venue":"Formal Methods in Computer Aided Design, FMCAD","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hutter et al., 2007b] Frank Hutter, Holger H. Hoos, and Thomas Stützle. Automatic algorithm configuration based on local search. In Proceedings of the Twenty-Second Con- ference on Artificial Intelligence, AAAI, pages 1152-1157. AAAI Press, 2007.","title":"Automatic algorithm configuration based on local search","authors":"Hutter","year":"2007","venue":"Proceedings of the Twenty-Second Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hutter et al., 2009] Frank Hutter, Holger H Hoos, Kevin Leyton-Brown, and Thomas Stützle. Paramils: an auto- matic algorithm configuration framework. Journal of Ar- tificial Intelligence Research, 36:267-306, 2009.","title":"Paramils: an automatic algorithm configuration framework","authors":"Frank Hutter, H Holger, Kevin Hoos, Thomas Leyton-Brown, Stützle","year":"2009","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hutter et al., 2019] Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, editors. Automated Machine Learn- ing -Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning. Springer, 2019.","title":"Automated Machine Learning -Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning","authors":"Hutter","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kerschke et al., 2019] Pascal Kerschke, Holger H. Hoos, Frank Neumann, and Heike Trautmann. Automated al- gorithm selection: Survey and perspectives. Evolutionary computation, 27(1):3-45, 2019.","title":"Automated algorithm selection: Survey and perspectives","authors":"Pascal Kerschke, H Holger, Frank Hoos, Heike Neumann, Trautmann","year":"2019","venue":"Evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kotthoff, 2016] Lars Kotthoff. Algorithm selection for combinatorial search problems: A survey. In Data Mining and Constraint Programming, pages 149-190. Springer, 2016.","title":"Algorithm selection for combinatorial search problems: A survey","authors":"Lars Kotthoff, Kotthoff","year":"2016","venue":"Data Mining and Constraint Programming","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lindauer and Hutter, 2018] Marius Lindauer and Frank Hutter. Warmstarting of model-based algorithm configura- tion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.","title":"Warmstarting of model-based algorithm configuration","authors":"Hutter Lindauer, Marius Lindauer, Frank Hutter","year":"2018","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Luo, 2016] Gang Luo. A review of automatic selec- tion methods for machine learning algorithms and hyper- parameter values. Network Modeling Analysis in Health Informatics and Bioinformatics, 5(1):1-16, 2016.","title":"Gang Luo. A review of automatic selection methods for machine learning algorithms and hyperparameter values","authors":"Luo","year":"2016","venue":"Network Modeling Analysis in Health Informatics and Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Marques-Silva, 2008] Joao Marques-Silva. Practical appli- cations of boolean satisfiability. In 9th International Work- shop on Discrete Event Systems, pages 74-80. IEEE, 2008.","title":"Practical applications of boolean satisfiability","authors":"Joao Marques-Silva","year":"2008","venue":"9th International Workshop on Discrete Event Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Schede et al., 2022] Elias Schede, Jasmin Brandt, Alexan- der Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. A survey of methods for automated algorithm configuration. Journal of Artificial Intelligence Research, 75:425-487, 2022.","title":"A survey of methods for automated algorithm configuration","authors":"Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney","year":"2022","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Stützle and López-Ibáñez, 2019] Thomas Stützle and Manuel López-Ibáñez. Automated design of metaheuris- tic algorithms. In Michel Gendreau and Jean-Yves Potvin, editors, Handbook of Metaheuristics, volume 272 of International Series in Operations Research & Management Science, pages 541-579. Springer, 2019.","title":"Automated design of metaheuristic algorithms","authors":"Thomas Stützle, Manuel López-Ibáñez","year":"2019","venue":"Handbook of Metaheuristics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Thornton et al., 2013] Chris Thornton, Frank Hutter, Hol- ger H. Hoos, and Kevin Leyton-Brown. Auto-weka: com- bined selection and hyperparameter optimization of classi- fication algorithms. In Inderjit S. Dhillon, Yehuda Koren, Rayid Ghani, Ted E. Senator, Paul Bradley, Rajesh Parekh, Jingrui He, Robert L. Grossman, and Ramasamy Uthu- rusamy, editors, The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013, pages 847-855. ACM, 2013.","title":"Auto-weka: combined selection and hyperparameter optimization of classification algorithms","authors":"Chris Thornton, Frank Hutter, H Holger, Kevin Hoos, Leyton-Brown","year":"2013","venue":"The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tornede et al., 2020] Alexander Tornede, Marcel Wever, and Eyke Hüllermeier. Extreme algorithm selection with dyadic feature representation. In Discovery Science -23rd International Conference, DS, pages 309-324, 2020.","title":"Extreme algorithm selection with dyadic feature representation","authors":"Alexander Tornede, Marcel Wever, Eyke Hüllermeier","year":"2020","venue":"Discovery Science -23rd International Conference","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang and Shami, 2020] Li Yang and Abdallah Shami. On hyperparameter optimization of machine learning algo- rithms: Theory and practice. Neurocomputing, 415:295- 316, 2020.","title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","authors":"Li Yang, Abdallah Shami","year":"2020","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yu and Zhu, 2020] Tong Yu and Hong Zhu. Hyper- parameter optimization: A review of algorithms and ap- plications. arXiv preprint arXiv:2003.05689, 2020.","title":"Hyperparameter optimization: A review of algorithms and applications","authors":"Tong Yu, Hong Zhu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zöller and Huber, 2021] Marc-André Zöller and Marco F. Huber. Benchmark and survey of automated machine learning frameworks. J. Artif. Intell. Res., 70:409-472, 2021.","title":"Benchmark and survey of automated machine learning frameworks","authors":"Huber ; Marc-André Zöller, Marco F Zöller, Huber","year":"2021","venue":"J. Artif. Intell. Res","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}