{"type":"ScholarlyArticle","title":"Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation","authors":[{"name":"Xingyu Wu","slug":"xingyu-wu","given":"Xingyu","family":"Wu","affiliation":"The Hong Kong Polytechnic University"},{"name":"Yan Zhong","slug":"yan-zhong","given":"Yan","family":"Zhong","affiliation":"Peking University"},{"name":"Jibin Wu","slug":"jibin-wu","given":"Jibin","family":"Wu","affiliation":"The Hong Kong Polytechnic University"},{"name":"Bingbing Jiang","slug":"bingbing-jiang","given":"Bingbing","family":"Jiang","affiliation":"Hangzhou Normal University"},{"name":"Kay Chen Tan","slug":"kay-chen-tan","given":"Kay Chen","family":"Tan","affiliation":"The Hong Kong Polytechnic University"}],"abstract":"Algorithm selection, a critical process of automated machine learning, aims to identify the most suitable algorithm for solving a specific problem prior to execution. Mainstream algorithm selection techniques heavily rely on problem features, while the role of algorithm features remains largely unexplored. Due to the intrinsic complexity of algorithms, effective methods for universally extracting algorithm information are lacking. This paper takes a significant step towards bridging this gap by introducing Large Language Models (LLMs) into algorithm selection for the first time. By comprehending the code text, LLM not only captures the structural and semantic aspects of the algorithm, but also demonstrates contextual awareness and library function understanding. The high-dimensional algorithm representation extracted by LLM, after undergoing a feature selection module, is combined with the problem representation and passed to the similarity calculation module. The selected algorithm is determined by the matching degree between a given problem and different algorithms. Extensive experiments validate the performance superiority of the proposed model and the efficacy of each key module. Furthermore, we present a theoretical upper bound on model complexity, showcasing the influence of algorithm representation and feature selection modules. This provides valuable theoretical guidance for the practical implementation of our method.","doi":"10.24963/ijcai.2024/579","url":"https://ijcai.org/proceedings/2024/579","pdf":"https://ijcai.org/proceedings/2024/0579.pdf","markdown":"https://ijcai.org/proceedings/2024/579.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/579","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":579,"pages":{"first":5235,"last":5244},"keywords":["Machine Learning: ML: Applications","Machine Learning: ML: Automated machine learning","Natural Language Processing: NLP: Language models","Search: S: Algorithm portfolios and configuration"],"videos":[],"references":[{"position":1,"raw":"[Abdulrahman et al., 2018] Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N van Rijn, and Joaquin Vanschoren. Speeding up algorithm selection using average ranking and active testing by introducing runtime. Machine learn- ing, 107:79-108, 2018.","title":"Speeding up algorithm selection using average ranking and active testing by introducing runtime","authors":"Pavel Salisu Mamman Abdulrahman, Jan N Brazdil, Joaquin Van Rijn, Vanschoren","year":"2018","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Alissa et al., 2023] Mohamad Alissa, Kevin Sim, and Em- ma Hart. Automated algorithm selection: from feature- based to feature-free approaches. Journal of Heuristics, 29(1):1-38, 2023.","title":"Automated algorithm selection: from featurebased to feature-free approaches","authors":"Mohamad Alissa, Kevin Sim, Emma Hart","year":"2023","venue":"Journal of Heuristics","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Amadini et al., 2014] Roberto Amadini, Maurizio Gab- brielli, and Jacopo Mauro. Sunny: a lazy portfolio ap- proach for constraint solving. Theory and Practice of Log- ic Programming, 14(4-5):509-524, 2014.","title":"Sunny: a lazy portfolio approach for constraint solving","authors":"Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro","year":"2014","venue":"Theory and Practice of Logic Programming","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bischl et al., 2016] Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger Hoos, Frank Hutter, Kevin Leyton- Brown, Kevin Tierney, et al. Aslib: A benchmark library for algorithm selection. Artificial Intelligence, 237:41-58, 2016.","title":"Aslib: A benchmark library for algorithm selection","authors":"Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney","year":"2016","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2021] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.","title":"Evaluating large language models trained on code","authors":"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Collautti et al., 2013] Marco Collautti, Yuri Malitsky, Deepak Mehta, and Barry OSullivan. Snnap: Solver- based nearest neighbor for algorithm portfolios. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, pages 435-450. Springer, 2013.","title":"Snnap: Solverbased nearest neighbor for algorithm portfolios","authors":"Marco Collautti, Yuri Malitsky, Deepak Mehta, Barry Osullivan","year":"2013","venue":"Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cunha et al., 2018] Tiago Cunha, Carlos Soares, and An- dré CPLF de Carvalho. A label ranking approach for s- electing rankings of collaborative filtering algorithms. In Proceedings of the 33rd Annual ACM Symposium on Ap- plied Computing, pages 1393-1395, 2018.","title":"A label ranking approach for selecting rankings of collaborative filtering algorithms","authors":"Tiago Cunha, Carlos Soares, André Cplf De Carvalho","year":"2018","venue":"Proceedings of the 33rd Annual ACM Symposium on Applied Computing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fehring et al., 2022] Lukass Fehring, Jonas Manuel Hanselle, and Alexander Tornede. Harris: Hybrid ranking and regression forests for algorithm selection. In NeurIPS 2022 Workshop on Meta-Learning (MetaLearn 2022), 2022.","title":"Harris: Hybrid ranking and regression forests for algorithm selection","authors":"Fehring","year":"2022","venue":"NeurIPS 2022 Workshop on Meta-Learning","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Feng et al., 2020] Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155, 2020.","title":"Codebert: A pre-trained model for programming and natural languages","authors":"Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Floridi and Chiriatti, 2020] Luciano Floridi and Massimo Chiriatti. Gpt-3: Its nature, scope, limits, and conse- quences. Minds and Machines, 30:681-694, 2020.","title":"Luciano Floridi and Massimo Chiriatti. Gpt-3: Its nature, scope, limits, and consequences","authors":"Chiriatti Floridi","year":"2020","venue":"Minds and Machines","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fusi et al., 2018] Nicolo Fusi, Rishit Sheth, and Melih Eli- bol. Probabilistic matrix factorization for automated ma- chine learning. Proceedings of the Advances Conference in Neural Information Processing Systems, 31, 2018.","title":"Probabilistic matrix factorization for automated machine learning","authors":"Nicolo Fusi, Rishit Sheth, Melih Elibol","year":"2018","venue":"Proceedings of the Advances Conference in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Golowich et al., 2018] Noah Golowich, Alexander Rakhlin, and Ohad Shamir. Size-independent sample complexity of neural networks. In Proceedings of the 31st Annual Conference On Learning Theory, pages 297-299. PMLR, 2018.","title":"Size-independent sample complexity of neural networks","authors":"Golowich","year":"2018","venue":"Proceedings of the 31st Annual Conference On Learning Theory","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Guo et al., 2020] Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. Graphcodebert: Pre-training code representations with data flow. In Pro- ceedings of the 8th International Conference on Learning Representations, 2020.","title":"Graphcodebert: Pre-training code representations with data flow","authors":"Shuo Daya Guo, Shuai Ren, Zhangyin Lu, Duyu Feng, Tang, Long Liu Shujie, Nan Zhou, Alexey Duan, Shengyu Svyatkovskiy, Fu","year":"2020","venue":"Proceedings of the 8th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Guo et al., 2022] Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. Unixcoder: Unified cross-modal pre-training for code representation. In Pro- ceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7212-7225, 2022.","title":"Unixcoder: Unified cross-modal pre-training for code representation","authors":"Shuai Daya Guo, Nan Lu, Yanlin Duan, Ming Wang, Jian Zhou, Yin","year":"2022","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hanselle, 2020] Jonas Hanselle. Hybrid ranking and regres- sion for algorithm selection. In German Conference on Artificial Intelligence, pages 59-72. Springer, 2020.","title":"Jonas Hanselle. Hybrid ranking and regression for algorithm selection","authors":"Hanselle","year":"2020","venue":"German Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hansen and Ostermeier, 2001] Nikolaus Hansen and An- dreas Ostermeier. Completely derandomized self- adaptation in evolution strategies. Evolutionary Compu- tation, 9(2):159-195, 2001.","title":"Completely derandomized selfadaptation in evolution strategies","authors":"Ostermeier Hansen","year":"2001","venue":"Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Heins et al., 2023] Jonathan Heins, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Ker- schke. A study on the effects of normalized tsp features for automated algorithm selection. Theoretical Computer Science, 940:123-145, 2023.","title":"A study on the effects of normalized tsp features for automated algorithm selection","authors":"Jonathan Heins, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, Pascal Kerschke","year":"2023","venue":"Theoretical Computer Science","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hilario et al., 2009] Melanie Hilario, Alexandros Kalousis, Phong Nguyen, and Adam Woznica. A data mining ontol- ogy for algorithm selection and meta-mining. In Proceed- ings of the ECML/PKDD09 Workshop on 3rd generation Data Mining (SoKD-09), pages 76-87. Citeseer, 2009.","title":"A data mining ontology for algorithm selection and meta-mining","authors":"Melanie Hilario, Alexandros Kalousis, Phong Nguyen, Adam Woznica","year":"2009","venue":"Proceedings of the ECML/PKDD09 Workshop on 3rd generation Data Mining (SoKD-09)","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hough and Williams, 2006] Patricia Diane Hough and Pamela J Williams. Modern machine learning for auto- matic optimization algorithm selection. Technical report, Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2006.","title":"Modern machine learning for automatic optimization algorithm selection","authors":"Patricia, Diane Hough, Pamela J Williams","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Hutter et al., 2014] Frank Hutter, Lin Xu, Holger H Hoos, and Kevin Leyton-Brown. Algorithm runtime prediction: Methods & evaluation. Artificial Intelligence, 206:79-111, 2014.","title":"Algorithm runtime prediction: Methods & evaluation","authors":"Frank Hutter, Lin Xu, Kevin Holger H Hoos, Leyton-Brown","year":"2014","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Jang et al., 2016] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In Proceedings of the International Conference on Learning Representations, 2016.","title":"Categorical reparameterization with gumbel-softmax","authors":"Eric Jang, Shixiang Gu, Ben Poole","year":"2016","venue":"Proceedings of the International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kadioglu et al., 2010] Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann, and Kevin Tierney. Isac-instance- specific algorithm configuration. In Proceedings of the 19th European Conference on Artificial Intelligence, pages 751-756. IOS Press, 2010.","title":"Isac-instancespecific algorithm configuration","authors":"Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann, Kevin Tierney","year":"2010","venue":"Proceedings of the 19th European Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"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":24,"raw":"[Koltchinskii, 2001] Vladimir Koltchinskii. Rademacher penalties and structural risk minimization. IEEE Trans- actions on Information Theory, 47(5):1902-1914, 2001.","title":"Rademacher penalties and structural risk minimization","authors":"Vladimir Koltchinskii","year":"2001","venue":"IEEE Transactions on Information Theory","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[McDiarmid and others, 1989] Colin McDiarmid et al. On the method of bounded differences. Surveys in Combina- torics, 141(1):148-188, 1989.","title":"On the method of bounded differences","authors":"Colin Mcdiarmid","year":"1989","venue":"Surveys in Combinatorics","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Mısır and Sebag, 2017] Mustafa Mısır and Michèle Sebag. Alors: An algorithm recommender system. Artificial In- telligence, 244:291-314, 2017.","title":"Alors: An algorithm recommender system","authors":"Mustafa Mısır, Michèle Sebag","year":"2017","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Nobel et al., 2021] Jacob Nobel, Hao Wang, and Thomas Baeck. Explorative data analysis of time series based al- gorithm features of cma-es variants. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 510-518, 2021.","title":"Explorative data analysis of time series based algorithm features of cma-es variants","authors":"Jacob Nobel, Hao Wang, Thomas Baeck","year":"2021","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ouyang et al., 2022] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instruction- s with human feedback. Proceedings of the 35th Annual Conference on Advances in Neural Information Process- ing Systems, 35:27730-27744, 2022.","title":"Training language models to follow instructions with human feedback","authors":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray","year":"2022","venue":"Proceedings of the 35th Annual Conference on Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Pio et al., 2023] Pedro B Pio, Adriano Rivolli, André C- PLF de Carvalho, and Luís PF Garcia. A review on prepro- cessing algorithm selection with meta-learning. Knowl- edge and Information Systems, pages 1-28, 2023.","title":"A review on preprocessing algorithm selection with meta-learning","authors":"Adriano Pedro B Pio, Rivolli, C-Plf De André, Luís Pf Carvalho, Garcia","year":"2023","venue":"Knowledge and Information Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Pulatov et al., 2022] Damir Pulatov, Marie Anastacio, Lars Kotthoff, and Holger Hoos. Opening the black box: Auto- mated software analysis for algorithm selection. In Pro- ceedings of the International Conference on Automated Machine Learning, pages 6-1. PMLR, 2022.","title":"Opening the black box: Automated software analysis for algorithm selection","authors":"Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger Hoos","year":"2022","venue":"Proceedings of the International Conference on Automated Machine Learning","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rice, 1976] John R Rice. The algorithm selection problem. In Advances in Computers, volume 15, pages 65-118.","title":"The algorithm selection problem","authors":"John R Rice","year":null,"venue":"Advances in Computers","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ruhkopf et al., 2022] Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, and Marius Lin- dauer. Masif: Meta-learned algorithm selection using implicit fidelity information. Transactions on Machine Learning Research, 2022.","title":"Masif: Meta-learned algorithm selection using implicit fidelity information","authors":"Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer","year":"2022","venue":"Transactions on Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Seiler et al., 2024] Moritz Vinzent Seiler, Pascal Kerschke, and Heike Trautmann. Deep-ela: Deep exploratory land- scape analysis with self-supervised pretrained transform- ers for single-and multi-objective continuous optimization problems. arXiv preprint arXiv:2401.01192, 2024.","title":"Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems","authors":"Seiler","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Tornede et al., 2020] Alexander Tornede, Marcel Wever, and Eyke Hüllermeier. Extreme algorithm selection with dyadic feature representation. In Proceedings of the In- ternational Conference on Discovery Science, pages 309- 324. Springer, 2020.","title":"Extreme algorithm selection with dyadic feature representation","authors":"Tornede","year":"2020","venue":"Proceedings of the International Conference on Discovery Science","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2022] Yejing Wang, Xiangyu Zhao, Tong Xu, and Xian Wu. Autofield: Automating feature selection in deep recommender systems. In Proceedings of the ACM Web Conference 2022, pages 1977-1986, 2022.","title":"Autofield: Automating feature selection in deep recommender systems","authors":"Wang","year":"2022","venue":"Proceedings of the ACM Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wu et al., 2020a] Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, et al. Accurate markov boundary dis- covery for causal feature selection. IEEE Transactions on Cybernetics, 50(12):4983-4996, 2020.","title":"Accurate markov boundary discovery for causal feature selection","authors":"Wu","year":"2020","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wu et al., 2020b] Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, and Chunyan Miao. Multi-label causal feature selection. In Proceedings of the AAAI conference on artificial intelligence, pages 6430-6437, 2020.","title":"Multi-label causal feature selection","authors":"Wu","year":"2020","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wu et al., 2023] Xingyu Wu, Bingbing Jiang, Yan Zhong, and Huanhuan Chen. Multi-target markov boundary dis- covery: Theory, algorithm, and application. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 45(4):4964-4980, 2023.","title":"Multi-target markov boundary discovery: Theory, algorithm, and application","authors":"Xingyu Wu, Bingbing Jiang, Yan Zhong, Huanhuan Chen","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wu et al., 2024] Xingyu Wu, Sheng-hao Wu, Jibin Wu, Liang Feng, and Kay Chen Tan. Evolutionary computation in the era of large language model: Survey and roadmap. arXiv preprint arXiv:2401.10034, 2024.","title":"Evolutionary computation in the era of large language model: Survey and roadmap","authors":"Xingyu Wu, Sheng-Hao Wu, Jibin Wu, Liang Feng, Kay Chen, Tan","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xiao et al., 2023] Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighof. C-pack: Packaged resources to advance general chinese embedding. arXiv preprint arX- iv:2309.07597, 2023.","title":"C-pack: Packaged resources to advance general chinese embedding","authors":"Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighof","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xu et al., 2008] Lin Xu, Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Satzilla: portfolio-based algorith- m selection for sat. Journal of Artificial Intelligence Re- search, 32:565-606, 2008.","title":"Satzilla: portfolio-based algorithm selection for sat","authors":"Lin Xu, Frank Hutter, Kevin Holger H Hoos, Leyton-Brown","year":"2008","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xu et al., 2011] Lin Xu, Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Hydra-mip: Automated algorith- m configuration and selection for mixed integer program- ming. In Proceedings of Workshop at the 32nd Interna- tional Joint Conference on Artificial Intelligence, pages 16-30, 2011.","title":"Hydra-mip: Automated algorithm configuration and selection for mixed integer programming","authors":"Lin Xu, Frank Hutter, Kevin Holger H Hoos, Leyton-Brown","year":"2011","venue":"Proceedings of Workshop at the 32nd International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhao et al., 2021] Kangfei Zhao, Shengcai Liu, Jeffrey Xu Yu, and Yu Rong. Towards feature-free tsp solver selec- tion: A deep learning approach. In Proceedings of the 2021 International Joint Conference on Neural Networks, pages 1-8. IEEE, 2021.","title":"Towards feature-free tsp solver selection: A deep learning approach","authors":"Zhao","year":"2021","venue":"Proceedings of the 2021 International Joint Conference on Neural Networks","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}