{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:23:22Z","timestamp":1783121002190,"version":"3.54.6"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/M004252\/1"],"award-info":[{"award-number":["EP\/M004252\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.artint.2026.104560","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T23:55:24Z","timestamp":1778889324000},"page":"104560","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Selection hyper-heuristics can automatically adjust the learning period to optimally solve pseudo-Boolean problems"],"prefix":"10.1016","volume":"357","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9786-220X","authenticated-orcid":false,"given":"Benjamin","family":"Doerr","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8164-6767","authenticated-orcid":false,"given":"Pietro S.","family":"Oliveto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6274-2638","authenticated-orcid":false,"given":"John Alasdair","family":"Warwicker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.artint.2026.104560_bib0001","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"1015","article-title":"On the runtime analysis of selection hyper-heuristics with adaptive learning periods","author":"Doerr","year":"2018"},{"issue":"1","key":"10.1016\/j.artint.2026.104560_bib0002","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.artint.2026.104560_bib0003","series-title":"Proceedings of the International Conference on Practice and Theory of Automated Timetabling","first-page":"176","article-title":"A hyperheuristic approach to scheduling a sales summit","author":"Cowling","year":"2001"},{"key":"10.1016\/j.artint.2026.104560_bib0004","series-title":"Proceedings of the Workshop on Applications of Evolutionary Computing","first-page":"1","article-title":"Hyperheuristics: a tool for rapid prototyping in scheduling and optimisation","author":"Cowling","year":"2002"},{"issue":"1","key":"10.1016\/j.artint.2026.104560_bib0005","doi-asserted-by":"crossref","first-page":"39","DOI":"10.4018\/jamc.2010102603","article-title":"A reinforcement learning - great-deluge hyper-heuristic for examination timetabling","volume":"1","author":"\u00d6zcan","year":"2010","journal-title":"Appl. Metaheuristic Comput."},{"key":"10.1016\/j.artint.2026.104560_bib0006","series-title":"Proceedings of the IEEE Symposium on Evolving and Autonomous Learning Systems","first-page":"65","article-title":"An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex","author":"Asta","year":"2014"},{"key":"10.1016\/j.artint.2026.104560_bib0007","series-title":"Handbook of Metaheuristics","first-page":"449","article-title":"A classification of hyper-heuristic approaches","author":"Burke","year":"2010"},{"key":"10.1016\/j.artint.2026.104560_bib0008","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1057\/jors.2013.71","article-title":"Hyper-heuristics: a survey of the state of the art","author":"Burke","year":"2013","journal-title":"J. Oper. Res. Soc."},{"key":"10.1016\/j.artint.2026.104560_bib0009","series-title":"Hyper-Heuristics: theory and Applications","author":"Pillay","year":"2018"},{"issue":"2","key":"10.1016\/j.artint.2026.104560_bib0010","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ejor.2019.07.073","article-title":"Recent advances in selection hyper-heuristics","volume":"285","author":"Drake","year":"2020","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.artint.2026.104560_bib0011","series-title":"Proceedings of the International Workshop on Foundations of Genetic Algorithms","first-page":"97","article-title":"A runtime analysis of simple hyper-heuristics: to mix or not to mix operators","author":"Lehre","year":"2013"},{"key":"10.1016\/j.artint.2026.104560_bib0012","series-title":"Proceedings of the IEEE Congress on Evolutionary Computation","first-page":"2515","article-title":"Runtime analysis of selection hyper-heuristics with classical learning mechanisms","author":"Alanazi","year":"2014"},{"key":"10.1016\/j.artint.2026.104560_bib0013","series-title":"Parallel Problem Solving from Nature","first-page":"835","article-title":"Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization","author":"Qian","year":"2016"},{"issue":"3","key":"10.1016\/j.artint.2026.104560_sbref0014","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1162\/evco_a_00258","article-title":"Simple hyper-heuristics control the neighbourhood size of randomised local search optimally for LeadingOnes","volume":"28","author":"Lissovoi","year":"2020","journal-title":"Evol. Comput."},{"issue":"1","key":"10.1016\/j.artint.2026.104560_bib0015","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1023\/A:1015059928466","article-title":"Evolution strategies - a comprehensive introduction","volume":"1","author":"Beyer","year":"2002","journal-title":"Natural Comput."},{"key":"10.1016\/j.artint.2026.104560_bib0016","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.tcs.2014.11.028","article-title":"From black-box complexity to designing new genetic algorithms","volume":"567","author":"Doerr","year":"2015","journal-title":"Theor. Comput. Sci."},{"key":"10.1016\/j.artint.2026.104560_bib0017","first-page":"1","article-title":"Optimal static and self-adjusting parameter choices for the (1+(\u03bb,\u03bb)) genetic algorithm","author":"Doerr","year":"2017","journal-title":"Algorithmica"},{"key":"10.1016\/j.artint.2026.104560_bib0018","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"889","article-title":"Hyper-parameter tuning for the (1 + (\u03bb, \u03bb)) GA","author":"Dang","year":"2019"},{"key":"10.1016\/j.artint.2026.104560_bib0019","series-title":"Theory of Evolutionary Computation - Recent Developments in Discrete Optimization","first-page":"271","article-title":"Theory of parameter control for discrete black-box optimization: provable performance gains through dynamic parameter choices","author":"Doerr","year":"2020"},{"key":"10.1016\/j.artint.2026.104560_bib0020","series-title":"Proceedings of the International Workshop on Foundations of Genetic Algorithms","first-page":"181","article-title":"Adaptive population models for offspring populations and parallel evolutionary algorithms","author":"L\u00e4ssig","year":"2011"},{"key":"10.1016\/j.artint.2026.104560_bib0021","series-title":"Proceedings of the International Conference on Parallel Problem Solving from Nature","first-page":"782","article-title":"Provably optimal self-adjusting step sizes for multi-valued decision variables","author":"Doerr","year":"2016"},{"key":"10.1016\/j.artint.2026.104560_bib0022","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s00453-018-0502-x","article-title":"The (1+\u03bb) evolutionary algorithm with self-adjusting mutation rate","volume":"81","author":"Doerr","year":"2019","journal-title":"Algorithmica"},{"key":"10.1016\/j.artint.2026.104560_bib0023","series-title":"Proceedings of the Genetic and Evolutionary Computation Conferenc","first-page":"1178","article-title":"Stagnation detection in highly multimodal fitness landscapes","author":"Rajabi","year":"2021"},{"key":"10.1016\/j.artint.2026.104560_bib0024","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1007\/s00453-022-00933-z","article-title":"Self-adjusting evolutionary algorithms for multimodal optimization","volume":"84","author":"Rajabi","year":"2022","journal-title":"Algorithmica"},{"issue":"1","key":"10.1016\/j.artint.2026.104560_bib0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/evco_a_00313","article-title":"Stagnation detection with randomized local search","volume":"31","author":"Rajabi","year":"2023","journal-title":"Evol. Comput."},{"key":"10.1016\/j.artint.2026.104560_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.tcs.2022.12.020","article-title":"Stagnation detection meets fast mutation","volume":"946","author":"Doerr","year":"2023","journal-title":"Theor. Comput. Sci."},{"key":"10.1016\/j.artint.2026.104560_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2023.104061","article-title":"Self-adjusting offspring population sizes outperform fixed parameters on the cliff function","volume":"328","author":"Hevia Fajardo","year":"2024","journal-title":"Artif. Intell."},{"key":"10.1016\/j.artint.2026.104560_bib0028","series-title":"Proceedings of the International Conference on Parallel Problem Solving from Nature","first-page":"803","article-title":"Self-adaptation of mutation rates in non-elitist populations","author":"Dang","year":"2016"},{"issue":"4","key":"10.1016\/j.artint.2026.104560_bib0029","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1007\/s00453-020-00726-2","article-title":"Runtime analysis for self-adaptive mutation rates","volume":"83","author":"Doerr","year":"2021","journal-title":"Algorithmica"},{"key":"10.1016\/j.artint.2026.104560_bib0030","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"1619","article-title":"Self-adaptation can help evolutionary algorithms track dynamic optima","author":"Lehre","year":"2023"},{"key":"10.1016\/j.artint.2026.104560_bib0031","series-title":"Proceedings of the International Workshop on Foundations of Genetic Algorithms","first-page":"105","article-title":"Self-adaptation can improve the noise-tolerance of evolutionary algorithms","author":"Lehre","year":"2023"},{"key":"10.1016\/j.artint.2026.104560_bib0032","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","article-title":"A self-adaptive coevolutionary algorithm","author":"Hevia Fajardo","year":"2024"},{"key":"10.1016\/j.artint.2026.104560_bib0033","series-title":"Proceedings of the European Conference on Evolutionary Computation in Combinatorial Optimization","first-page":"170","article-title":"Limits to learning in reinforcement learning hyper-heuristics","author":"Alanazi","year":"2016"},{"key":"10.1016\/j.artint.2026.104560_bib0034","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"766","article-title":"Theory-inspired parameter control benchmarks for dynamic algorithm configuration","author":"Biedenkapp","year":"2022"},{"key":"10.1016\/j.artint.2026.104560_bib0035","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"1622","article-title":"Random gradient hyper-heuristics can learn to Escape local optima in multimodal optimisation","author":"Ma","year":"2025"},{"key":"10.1016\/j.artint.2026.104560_bib0036","series-title":"Proceedings of the International Conference on Parallel Problem Solving from Nature","first-page":"824","article-title":"k-bit mutation with self-adjusting k outperforms standard bit mutation","author":"Doerr","year":"2016"},{"key":"10.1016\/j.artint.2026.104560_bib0037","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"1578","article-title":"A flexible evolutionary algorithm with dynamic mutation rate","author":"Krejca","year":"2024"},{"key":"10.1016\/j.artint.2026.104560_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2022.103804","article-title":"When move acceptance selection hyper-heuristics outperform metropolis and elitist evolutionary algorithms and when not","volume":"314","author":"Lissovoi","year":"2023","journal-title":"Artif. Intell."},{"key":"10.1016\/j.artint.2026.104560_bib0039","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"990","article-title":"How the move acceptance hyper-heuristic copes with local optima: drastic differences between jumps and cliffs","author":"Doerr","year":"2023"},{"key":"10.1016\/j.artint.2026.104560_bib0040","unstructured":"B. Doerr, J.F. Lutzeyer, Hyper-heuristics can profit from global variation operators, (2024). arXiv: 2407.14237."},{"key":"10.1016\/j.artint.2026.104560_bib0041","series-title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","first-page":"8850","article-title":"Speeding up hyper-heuristics with Markov-Chain operator selection and the only-worsening acceptance operator","author":"Bendahi","year":"2025"},{"key":"10.1016\/j.artint.2026.104560_bib0042","series-title":"Automated Design of Machine Learning and Search Algorithms","first-page":"45","article-title":"Rigorous performance analysis of hyper-heuristics","author":"Oliveto","year":"2021"},{"key":"10.1016\/j.artint.2026.104560_bib0043","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1023\/B:NACO.0000023416.59689.4e","article-title":"Learning probability distributions in continuous evolutionary algorithms - a comparative review","volume":"3","author":"Kern","year":"2004","journal-title":"Natural Comput."},{"key":"10.1016\/j.artint.2026.104560_bib0044","series-title":"Proceedings of the Annual Conference on Genetic and Evolutionary Computation","first-page":"1335","article-title":"Optimal Parameter Choices Through Self-Adjustment: Applying the 1\/5th Rule in Discrete Settings","author":"Doerr","year":"2015"},{"key":"10.1016\/j.artint.2026.104560_bib0045","series-title":"Proceedings of the Genetic and Evolutionary Computation Conference","first-page":"943","article-title":"Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problems","author":"Doerr","year":"2018"},{"key":"10.1016\/j.artint.2026.104560_bib0046","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.tcs.2018.09.024","article-title":"Analyzing randomized search heuristics via stochastic domination","volume":"773","author":"Doerr","year":"2019","journal-title":"Theor. Comput. Sci."},{"key":"10.1016\/j.artint.2026.104560_bib0047","series-title":"Proceedings of the International Conference on Parallel Problem Solving from Nature","first-page":"1","article-title":"Optimal fixed and adaptive mutation rates for the LeadingOnes problem","author":"B\u00f6ttcher","year":"2010"},{"key":"10.1016\/j.artint.2026.104560_bib0048","series-title":"Theory of Randomized Search Heuristics","first-page":"1","article-title":"Analyzing randomized search heuristics: tools from probability theory","author":"Doerr","year":"2011"},{"key":"10.1016\/j.artint.2026.104560_bib0049","series-title":"Proceedings of the International Workshop on Foundations of Genetic Algorithms","first-page":"40","article-title":"(1+1) EA on generalized dynamic OneMax","author":"K\u00f6tzing","year":"2015"},{"key":"10.1016\/j.artint.2026.104560_bib0050","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/01621459.1963.10500830","article-title":"Probability inequalities for sums of bounded random variables","volume":"58","author":"Hoeffding","year":"1963","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.artint.2026.104560_bib0051","series-title":"Handbook of Heuristics","first-page":"1131","article-title":"Time complexity analysis of stochastic search algorithms","author":"Oliveto","year":"2025"},{"key":"10.1016\/j.artint.2026.104560_bib0052","series-title":"Analyzing Evolutionary Algorithms: The Computer Science Perspective","author":"Jansen","year":"2013"},{"key":"10.1016\/j.artint.2026.104560_bib0053","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"2376","article-title":"How the duration of the learning period affects the performance of random gradient selection hyper-heuristics","author":"Lissovoi","year":"2020"}],"container-title":["Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S000437022600086X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S000437022600086X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:58:30Z","timestamp":1783119510000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S000437022600086X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":53,"alternative-id":["S000437022600086X"],"URL":"https:\/\/doi.org\/10.1016\/j.artint.2026.104560","relation":{},"ISSN":["0004-3702"],"issn-type":[{"value":"0004-3702","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Selection hyper-heuristics can automatically adjust the learning period to optimally solve pseudo-Boolean problems","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artint.2026.104560","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104560"}}