{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:23:09Z","timestamp":1774941789796,"version":"3.50.1"},"reference-count":25,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1109\/cec.2018.8477662","type":"proceedings-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T00:33:13Z","timestamp":1540254793000},"page":"1-8","source":"Crossref","is-referenced-by-count":13,"title":["Analysis of the Complexity of the Automatic Pipeline Generation Problem"],"prefix":"10.1109","author":[{"given":"Unai","family":"Garciarena","sequence":"first","affiliation":[]},{"given":"Roberto","family":"Santana","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Mendiburu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"1","article-title":"Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA","volume":"18","author":"kotthoff","year":"2017","journal-title":"Journal of Machine Learning Research"},{"key":"ref11","first-page":"507","article-title":"Sequential Model-Based Optimization for General Algorithm Configuration","volume":"5","author":"hutter","year":"2011","journal-title":"LION"},{"key":"ref12","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Advances in neural information processing systems"},{"key":"ref13","first-page":"115","article-title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","author":"bergstra","year":"2013","journal-title":"International Conference on Machine Learning"},{"key":"ref14","first-page":"123","author":"olson","year":"2016","journal-title":"Applications of Evolutionary Computation 19th European Conference Evo Applications 2016 Porto Portugal March 30 April 1 2016 Proceedings Part I"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2001.934438"},{"key":"ref16","author":"deb","year":"2001","journal-title":"Multi-Objective Optimization Using Evolutionary Algorithms"},{"key":"ref17","first-page":"2171","article-title":"DEAP: Evolutionary Algorithms Made Easy","volume":"13","author":"fortin","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1007\/978-3-319-55696-3_16","article-title":"RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines","author":"de s","year":"2017","journal-title":"European Conference on Genetic Programming"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1145\/2908812.2908918","article-title":"Evaluation of a tree-based pipeline optimization tool for automating data science","author":"olson","year":"2016","journal-title":"Proceedings of the 2016 on Genetic and Evolutionary Computation Conference"},{"key":"ref4","article-title":"Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn","author":"komer","year":"2014","journal-title":"ICML AutoML Workshop"},{"key":"ref3","author":"brochu","year":"2010","journal-title":"A Tutorial on Bayesian Optimization of Expensive Cost Functions with Application to Active User Modeling and Hierarchical Reinforcement Learning"},{"key":"ref6","author":"rijsbergen","year":"1979","journal-title":"Information Retrieval"},{"key":"ref5","first-page":"1","article-title":"Design of the 2015 ChaLearn AutoML challenge","author":"guyon","year":"2015","journal-title":"IEEE"},{"key":"ref8","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"The Journal of Machine Learning Research"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"ref2","first-page":"66","article-title":"TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning","author":"olson","year":"2016","journal-title":"Proceedings of the Workshop on Automatic Machine Learning"},{"key":"ref9","first-page":"2962","article-title":"Efficient and Robust Automated Machine Learning","author":"feurer","year":"2015","journal-title":"Curran Associates Inc"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"ref20","author":"garciarena","year":"2017","journal-title":"Evolving imputation strategies for missing data in classification problems with TPOT"},{"key":"ref22","author":"russell","year":"2003","journal-title":"Artificial Intelligence A Modern Approach"},{"key":"ref21","author":"garciarena","year":"2018","journal-title":"Towards a more efficient representation of imputation operators in TPOT"},{"key":"ref24","author":"dinno","year":"2017","journal-title":"dunn test Dunn's Test of Multiple Comparisons Using Rank Sums"},{"key":"ref23","author":"olson","year":"2016","journal-title":"PMLB Penn Machine Learning Benchmarks"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.2307\/2280779"}],"event":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","location":"Rio de Janeiro","start":{"date-parts":[[2018,7,8]]},"end":{"date-parts":[[2018,7,13]]}},"container-title":["2018 IEEE Congress on Evolutionary Computation (CEC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8466244\/8477640\/08477662.pdf?arnumber=8477662","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T07:50:47Z","timestamp":1693986647000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8477662\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/cec.2018.8477662","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}