{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T13:08:03Z","timestamp":1749042483912,"version":"3.40.3"},"publisher-location":"Cham","reference-count":152,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030720681"},{"type":"electronic","value":"9783030720698"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-72069-8_7","type":"book-chapter","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T18:02:55Z","timestamp":1627495375000},"page":"109-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hyper-heuristics: Autonomous Problem Solvers"],"prefix":"10.1007","author":[{"given":"Mustafa","family":"M\u0131s\u0131r","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,29]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"N. Acevedo, C. Rey, C. Contreras-Bolton, V. Parada, Automatic design of specialized algorithms for the binary knapsack problem. in Expert Systems with Applications (2019), p. 112908","DOI":"10.1016\/j.eswa.2019.112908"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"S. Adriaensen, G. Ochoa, A. Now\u2019e, A benchmark set extension and comparative study for the hyflex framework, in IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2015), pp. 784\u2013791","DOI":"10.1109\/CEC.2015.7256971"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"L. Ahmed, P. Heyken-Soares, C. Mumford, Y. Mao, Optimising bus routes with fixed terminal nodes: comparing hyper-heuristics with nsgaii on realistic transportation networks, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (ACM, 2019), pp. 1102\u20131110","DOI":"10.1145\/3321707.3321867"},{"issue":"2","key":"7_CR4","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/j.ejor.2018.10.022","volume":"274","author":"L Ahmed","year":"2019","unstructured":"L. Ahmed, C. Mumford, A. Kheiri, Solving urban transit route design problem using selection hyper-heuristics. Eur. J. Oper. Res. 274(2), 545\u2013559 (2019)","journal-title":"Eur. J. Oper. Res."},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"F. Alanazi, P.K. Lehre, Runtime analysis of selection hyper-heuristics with classical learning mechanisms, in IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2014), pp. 2515\u20132523","DOI":"10.1109\/CEC.2014.6900602"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"M.A. Ardeh, Y. Mei, M. Zhang, Transfer learning in genetic programming hyper-heuristic for solving uncertain capacitated arc routing problem, in 2019 IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2019), pp. 49\u201356","DOI":"10.1109\/CEC.2019.8789920"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"F. Assun\u00e7ao, N. Louren\u00e7o, P. Machado, B. Ribeiro, Automatic generation of neural networks with structured grammatical evolution, in IEEE Congress on Evolutionary Computation (CEC) (San Sebastian, Spain, 2017), pp. 1557\u20131564","DOI":"10.1109\/CEC.2017.7969488"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"F. Assun\u00e7\u00e3o, N. Louren\u00e7o, P. Machado, B. Ribeiro, Using gp is neat: evolving compositional pattern production functions, in European Conference on Genetic Programming (Springer, 2018), pp. 3\u201318","DOI":"10.1007\/978-3-319-77553-1_1"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.eswa.2016.07.005","volume":"63","author":"S Asta","year":"2016","unstructured":"S. Asta, E. \u00d6zcan, A.J. Parkes, CHAMP: creating heuristics via many parameters for online bin packing. Expert Syst. Appl. 63, 208\u2013221 (2016)","journal-title":"Expert Syst. Appl."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"I. Azaria, A. Elyasaf, M. Sipper, Evolving artificial general intelligence for video game controllers, in Genetic Programming Theory and Practice XIV (Springer, 2018), pp. 53\u201363","DOI":"10.1007\/978-3-319-97088-2_4"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Z.A. Aziz, Ant colony hyper-heuristics for travelling salesman problem. Procedia Comput. Sci. 76, 534\u2013538 (2015)","DOI":"10.1016\/j.procs.2015.12.333"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms (Oxford University Press, 1996)","DOI":"10.1093\/oso\/9780195099713.001.0001"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"T. B\u00e4ck, D.B. Fogel, Z. Michalewicz, Evolutionary Computation 1: Basic Algorithms and Operators (CRC press, 2018)","DOI":"10.1201\/9781482268713"},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.asoc.2015.06.063","volume":"36","author":"M Beyaz","year":"2015","unstructured":"M. Beyaz, T. Dokeroglu, A. Cosar, Robust hyper-heuristic algorithms for the offline oriented\/non-oriented 2d bin packing problems. Appl. Soft Comput. 36, 236\u2013245 (2015)","journal-title":"Appl. Soft Comput."},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"B.\u00a0Bilgin, P.\u00a0Demeester, M.\u00a0M\u0131s\u0131r, W.\u00a0Vancroonenburg, G.\u00a0Vanden Berghe, One hyperheuristic approach to two timetabling problems in health care. J. Heuristics 18(3), 401\u2013434 (2012)","DOI":"10.1007\/s10732-011-9192-0"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"I. Borgulya, A parallel hyper-heuristic approach for the two-dimensional rectangular strip-packing problem. CIT. J. Comput. Inf. Technol.\u00a022(4), 251\u2013265 (2014)","DOI":"10.2498\/cit.1002422"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"I. Borgulya, A parallel hyper-heuristic approach for the two-dimensional rectangular strip-packing problem. CIT. J. Comput. Inf. Technol.22(4), 251\u2013265 (2014)","DOI":"10.2498\/cit.1002422"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"E.\u00a0Burke, T.\u00a0Curtois, M.\u00a0Hyde, G.\u00a0Kendall, G.\u00a0Ochoa, S.\u00a0Petrovic, J.A. V\u00e1zquez-Rodr\u0131guez, M.\u00a0Gendreau, Iterated local search vs. hyper-heuristics: towards general-purpose search algorithms, in Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (Barcelona, Spain, July 18\u201323 2010), pp. 3073\u20133080","DOI":"10.1109\/CEC.2010.5586064"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"E.K. Burke, M.R. Hyde, G. Kendall, G. Ochoa, E. Ozcan, J.R. Woodward, Exploring hyper-heuristic methodologies with genetic programming, in Computational Intelligence (Springer, 2009), pp. 177\u2013201","DOI":"10.1007\/978-3-642-01799-5_6"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"E.K. Burke, M.R. Hyde, G. Kendall, G. Ochoa, E. \u00d6zcan, J.R. Woodward, A classification of hyper-heuristic approaches: revisited, in Handbook of Metaheuristics (Springer, 2019), pp. 453\u2013477","DOI":"10.1007\/978-3-319-91086-4_14"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"E.K. Burke, G. Kendall, M. M\u0131s\u0131r, E. \u00d6zcan, Monte carlo hyper-heuristics for examination timetabling. Ann. Oper. Res. 196(1), 73\u201390 (2012)","DOI":"10.1007\/s10479-010-0782-2"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"E.K. Burke, E. Hart, G. Kendall, J. Newall, P. Ross, S. Schulenburg, Hyper-heuristics: an emerging direction in modern search technology, in Handbook of Meta-Heuristics (Kluwer Academic Publishers, 2003), pp. 457\u2013474","DOI":"10.1007\/0-306-48056-5_16"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"E.K. Burke, M.\u00a0Hyde, G.\u00a0Kendall, G.\u00a0Ochoa, E.\u00a0Ozcan, J.R. Woodward, A classification of hyper-heuristic approaches, in Handbook of Metaheuristics (2010), pp. 449\u2013468","DOI":"10.1007\/978-1-4419-1665-5_15"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"E.K. Burke, M.R. Hyde, G. Kendall, Evolving bin packing heuristics with genetic programming, in Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN), vol. 4193. LNCS, ed. by T.P. Runarsson, H.-G. Beyer, E. Burke, J.J. Merelo-Guervos, L.D. Whitley, X. Yao (Springer, Reykjavik, Iceland, September 9\u201313 2006), pp. 860\u2013869","DOI":"10.1007\/11844297_87"},{"issue":"1","key":"7_CR25","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10479-010-0782-2","volume":"196","author":"EK Burke","year":"2012","unstructured":"E.K. Burke, G. Kendall, M. M\u0131s\u0131r, E. \u00d6zcan, Monte carlo hyper-heuristics for examination timetabling. Ann. Oper. Res. 196(1), 73\u201390 (2012)","journal-title":"Ann. Oper. Res."},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"E.K. Burke, S.\u00a0Petrovic, R.\u00a0Qu, Case based heuristic selection for timetabling problems. J. Sched. 9(2), 115\u2013132 (2006)","DOI":"10.1007\/s10951-006-6775-y"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"F. Cabitza, R. Rasoini, G.F. Gensini, Unintended consequences of machine learning in medicine. Jama 318(6), 517\u2013518 (2017)","DOI":"10.1001\/jama.2017.7797"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"K.\u00a0Chakhlevitch, P.\u00a0Cowling, Choosing the fittest subset of low level heuristics in a hyperheuristic framework, in Proceedings of the 5th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP), vol. 3448. LNCS, ed. by G.R. Raidl, J.\u00a0Gottlieb (Springer, 2005), pp. 23\u201333","DOI":"10.1007\/978-3-540-31996-2_3"},{"key":"7_CR29","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ins.2017.12.013","volume":"432","author":"S Chand","year":"2018","unstructured":"S. Chand, Q. Huynh, H. Singh, T. Ray, M. Wagner, On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems. Inf. Sci. 432, 146\u2013163 (2018)","journal-title":"Inf. Sci."},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"S.N. Chaurasia, D. Jung, H.M. Lee, J.H. Kim, An evolutionary algorithm based hyper-heuristic for the set packing problem, in Harmony Search and Nature Inspired Optimization Algorithms (Springer, 2019), pp. 259\u2013268","DOI":"10.1007\/978-981-13-0761-4_26"},{"issue":"12","key":"7_CR31","doi-asserted-by":"publisher","first-page":"4937","DOI":"10.1007\/s10489-018-1250-y","volume":"48","author":"B Chen","year":"2018","unstructured":"B. Chen, Q. Rong, R. Bai, W. Laesanklang, A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows. Appl. Intell. 48(12), 4937\u20134959 (2018)","journal-title":"Appl. Intell."},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"S.S. Choong, L.-P. Wong, C.P. Lim, An artificial bee colony algorithm with a modified choice function for the traveling salesman problem, in IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, 2017)","DOI":"10.1109\/SMC.2017.8122629"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"P.\u00a0Cowling, G.\u00a0Kendall, E.\u00a0Soubeiga, A hyperheuristic approach to scheduling a sales summit, in PATAT\u201900: Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III (Springer, London, UK, 2001), pp. 176\u2013190","DOI":"10.1007\/3-540-44629-X_11"},{"key":"7_CR34","unstructured":"P.\u00a0Cowling, G.\u00a0Kendall, E.\u00a0Soubeiga, A parameter-free hyperheuristic for scheduling a sales summit, Ii Proceedings of 4th Metahuristics International Conference (MIC) (Porto, Portugal, July 16\u201320 2001), pp. 127\u2013131"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"L.\u00a0Da\u00a0Costa, A.\u00a0Fialho, M.\u00a0Schoenauer, M.\u00a0Sebag, Adaptive operator selection with dynamic multi-armed bandits, in Proceedings of Genetic and Evolutionary Computation Conference (GECCO) (Atlanta, Georgia, USA, 2008), pp. 913\u2013920","DOI":"10.1145\/1389095.1389272"},{"key":"7_CR36","doi-asserted-by":"crossref","unstructured":"K. Danach, S. Gelareh, R.N. Monemi, The capacitated single-allocation p-hub location routing problem: a lagrangian relaxation and a hyper-heuristic approach, in EURO Journal on Transportation and Logistics (2019), pp. 1\u201335","DOI":"10.1007\/s13676-019-00141-w"},{"key":"7_CR37","unstructured":"L. Davis, in Handbook of Genetic Algorithms (1991)"},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"J. de\u00a0Andrade, L. Silva, A. Britto, R. Amaral, Solving the software project scheduling problem with hyper-heuristics, in International Conference on Artificial Intelligence and Soft Computing (Springer, 2019), pp. 399\u2013411","DOI":"10.1007\/978-3-030-20912-4_37"},{"key":"7_CR39","doi-asserted-by":"crossref","unstructured":"J.\u00a0de Armas, G.\u00a0Miranda, C.\u00a0Le\u00f3n, Hyperheuristic encoding scheme for multi-objective guillotine cutting problems, in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (ACM, 2011), pp. 1683\u20131690","DOI":"10.1145\/2001576.2001803"},{"key":"7_CR40","doi-asserted-by":"crossref","unstructured":"P.\u00a0Demeester, B.\u00a0Bilgin, P.\u00a0De Causmaecker, G.\u00a0Vanden Berghe, A hyperheuristic approach to examination timetabling problems: benchmarks and a new problem from practice. J. Sched. 15(1) (2012)","DOI":"10.1007\/s10951-011-0258-5"},{"key":"7_CR41","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.engappai.2016.02.004","volume":"52","author":"T Dokeroglu","year":"2016","unstructured":"T. Dokeroglu, A. Cosar, A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem. Eng. Appl. Artif. Intell. 52, 10\u201325 (2016)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"7_CR42","doi-asserted-by":"crossref","unstructured":"D. Domovi\u0107, T. Rolich, M. Golub, Evolutionary hyper-heuristic for solving the strip-packing problem, in The Journal of The Textile Institute (2019), pp. 1\u201311","DOI":"10.1080\/00405000.2018.1550136"},{"key":"7_CR43","doi-asserted-by":"crossref","unstructured":"J.H. Drake, A. Kheiri, E. \u00d6zcan, E.K. Burke, Recent advances in selection hyper-heuristics, in European Journal of Operational Research (2019)","DOI":"10.1016\/j.ejor.2019.07.073"},{"key":"7_CR44","doi-asserted-by":"crossref","unstructured":"J.H. Drake, E. \u00d6zcan, E.K. Burke, A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem. Evol. Comput. 24(1), 113\u2013141 (2016)","DOI":"10.1162\/EVCO_a_00145"},{"key":"7_CR45","doi-asserted-by":"crossref","unstructured":"G. Duflo, E. Kieffer, M.R. Brust, G. Danoy, P. Bouvry, A gp hyper-heuristic approach for generating tsp heuristics, in 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (IEEE, 2019), pp. 521\u2013529","DOI":"10.1109\/IPDPSW.2019.00094"},{"issue":"13","key":"7_CR46","doi-asserted-by":"publisher","first-page":"5491","DOI":"10.1016\/j.eswa.2015.01.038","volume":"42","author":"A Elhag","year":"2015","unstructured":"A. Elhag, E. \u00d6zcan, A grouping hyper-heuristic framework: application on graph colouring. Expert Syst. Appl. 42(13), 5491\u20135507 (2015)","journal-title":"Expert Syst. Appl."},{"key":"7_CR47","doi-asserted-by":"crossref","unstructured":"I. Fajfar, J. Puhan, \u00c1. Burmen, Evolving a nelder-mead algorithm for optimization with genetic programming. Evolutionary computation (2016)","DOI":"10.1162\/evco_a_00174"},{"key":"7_CR48","doi-asserted-by":"crossref","unstructured":"V.D. Fontoura, A.T.R. Pozo, R. Santana, Automated design of hyper-heuristics components to solve the psp problem with hp model, in IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2017), pp. 1848\u20131855","DOI":"10.1109\/CEC.2017.7969526"},{"key":"7_CR49","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.ejor.2010.12.005","volume":"1","author":"A Garcia-Villoria","year":"2011","unstructured":"A. Garcia-Villoria, S. Salhi, A. Corominas, R. Pastor, Hyper-heuristic approaches for the response time variability problem. Eur. J. Oper. Res. 1, 160\u2013169 (2011)","journal-title":"Eur. J. Oper. Res."},{"key":"7_CR50","doi-asserted-by":"crossref","unstructured":"M. Gendreau, J.-Y. Potvin, in Handbook of Metaheuristics (Springer, 2019)","DOI":"10.1007\/978-3-319-91086-4"},{"key":"7_CR51","doi-asserted-by":"crossref","unstructured":"J.\u00a0Gibbs, G.\u00a0Kendall, E.\u00a0Ozcan Scheduling english football fixtures over the holiday period using hyper-heuristics, in Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN), vol. 6238. LNCS, ed. by R.\u00a0Schaefer, C.\u00a0Cotta, J.\u00a0Kolodziej, G.\u00a0Rudolph (Springer, Krakow, Poland, September 11\u201315 2010), pp. 496\u2013505","DOI":"10.1007\/978-3-642-15844-5_50"},{"key":"7_CR52","doi-asserted-by":"crossref","unstructured":"J.C. Gomez, H. Terashima-Mar\u00edn, Evolutionary hyper-heuristics for tackling bi-objective 2d bin packing problems. Genet. Program. Evolvable Mach. 19(1\u20132), 151\u2013181 (2018)","DOI":"10.1007\/s10710-017-9301-4"},{"key":"7_CR53","doi-asserted-by":"crossref","unstructured":"J. Grobler, A.P. Engelbrecht, G. Kendall, V.S.S. Yadavalli, Heuristic space diversity control for improved meta-hyper-heuristic performance. Inf. Sci. 300 (2015)","DOI":"10.1016\/j.ins.2014.11.012"},{"key":"7_CR54","unstructured":"G.D. Hager, D. Rus, V. Kumar, H. Christensen, Toward a science of autonomy for physical systems (2016). arXiv:1604.02979"},{"key":"7_CR55","doi-asserted-by":"crossref","unstructured":"P. Hansen, N. Mladenovi\u0107, J. Brimberg, J.A. Moreno P\u00e9rez, Variable neighborhood search, in Handbook of metaheuristics (Springer, 2019), pp. 57\u201397","DOI":"10.1007\/978-3-319-91086-4_3"},{"issue":"4","key":"7_CR56","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1162\/EVCO_a_00183","volume":"24","author":"E Hart","year":"2016","unstructured":"E. Hart, K. Sim, A hyper-heuristic ensemble method for static job-shop scheduling. Evol. Comput. 24(4), 609\u2013635 (2016)","journal-title":"Evol. Comput."},{"key":"7_CR57","doi-asserted-by":"crossref","unstructured":"J.\u00a0He, F.\u00a0He, H.\u00a0Dong, Pure strategy or mixed strategy? - an initial comparison of their asymptotic convergence rate and asymptotic hitting time, in Proceedings of the 12th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP), vol. 7245. LNCS, ed. by J.-K. Hao, M.\u00a0Middendorf (2012), pp. 218\u2013229","DOI":"10.1007\/978-3-642-29124-1_19"},{"key":"7_CR58","doi-asserted-by":"crossref","unstructured":"P. Hernandez, C. Gomez, L. Cruz, A. Ochoa, N. Castillo, G. Rivera, Hyperheuristic for the parameter tuning of a bio-inspired algorithm of query routing in p2p networks, in the 10th Mexican International Conference on Artificial Intelligence (MICAI, vol. 7095. Advances in Soft Computing, LNAI, ed. by I. Batyrshin, G. Sidorov (Springer, Berlin\/Heidelberg, 2011), pp. 119\u2013130","DOI":"10.1007\/978-3-642-25330-0_11"},{"key":"7_CR59","doi-asserted-by":"crossref","unstructured":"L. Hong, J.H. Drake, J.R. Woodward, E. \u00d6zcan, A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Appl. Soft Comput. 62, 162\u2013175 (2018)","DOI":"10.1016\/j.asoc.2017.10.002"},{"key":"7_CR60","doi-asserted-by":"crossref","unstructured":"F. Hutter, L. Kotthoff, J. Vanschoren, in Automated Machine Learning - Methods, Systems, Challenges (Springer, 2019)","DOI":"10.1007\/978-3-030-05318-5"},{"key":"7_CR61","doi-asserted-by":"crossref","unstructured":"J. Jacobsen-Grocott, Y. Mei, G. Chen, M. Zhang, Evolving heuristics for dynamic vehicle routing with time windows using genetic programming, in IEEE Congress on Evolutionary Computation (CEC) (San Sebastian, Spain, 2017)","DOI":"10.1109\/CEC.2017.7969539"},{"key":"7_CR62","doi-asserted-by":"crossref","unstructured":"H.L. Jakubovski\u00a0Filho, T.N. Ferreira, S.R. Vergilio, Incorporating user preferences in a software product line testing hyper-heuristic approach, in IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2018), pp. 1\u20138","DOI":"10.1109\/CEC.2018.8477803"},{"key":"7_CR63","doi-asserted-by":"crossref","unstructured":"G. Kendall, J. Li, Competitive travelling salesmen problem: a hyper-heuristic approach. J. Oper. Res. Soc. (2012)","DOI":"10.1057\/jors.2012.37"},{"key":"7_CR64","unstructured":"G.\u00a0Kendall, M.\u00a0Mohamad, Channel assignment optimisation using a hyper-heuristic, in Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems (CIS) (Singapore, December 1\u20133 2004), pp. 790\u2013795"},{"key":"7_CR65","doi-asserted-by":"crossref","unstructured":"P. Kerschke, H.H. Hoos, F. Neumann, H. Trautmann, Automated algorithm selection: survey and perspectives, in Evolutionary Computation (2018), pp. 1\u201347","DOI":"10.1162\/evco_a_00242"},{"key":"7_CR66","doi-asserted-by":"crossref","unstructured":"A. Kheiri, Ed.\u00a0Keedwell, A hidden markov model approach to the problem of heuristic selection in hyper-heuristics with a case study in high school timetabling problems. Evol. Comput. 25(3), 473\u2013501 (2017)","DOI":"10.1162\/evco_a_00186"},{"key":"7_CR67","unstructured":"A.R. KhudaBukhsh, L.\u00a0Xu, H.H. Hoos, K.\u00a0Leyton-Brown, Satenstein: automatically building local search sat solvers from components, in Proceedings of the 21th International Joint Conference on Artifical Intelligence (IJCAI\u201909) (2009), pp. 517\u2013524"},{"key":"7_CR68","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing. Science 220, 671\u2013680 (1983)","journal-title":"Science"},{"key":"7_CR69","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.ins.2014.02.155","volume":"277","author":"G Koulinas","year":"2014","unstructured":"G. Koulinas, L. Kotsikas, K. Anagnostopoulos, A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Inf. Sci. 277, 680\u2013693 (2014)","journal-title":"Inf. Sci."},{"key":"7_CR70","unstructured":"J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol.\u00a01 (MIT press, 1992)"},{"key":"7_CR71","doi-asserted-by":"crossref","unstructured":"R. Lahyani, A.-L. Gouguenheim, L.C. Coelho, A hybrid adaptive large neighbourhood search for multi-depot open vehicle routing problems. Int. J. Prod. Res., pp. 1\u201314 (2019)","DOI":"10.1080\/00207543.2019.1572929"},{"key":"7_CR72","doi-asserted-by":"crossref","unstructured":"P.K. Lehre, E. \u00d6zcan, A runtime analysis of simple hyper-heuristics: to mix or not to mix operators, in Proceedings of the 12th Workshop on Foundations of Genetic Algorithms (FOGA) (ACM, 2013), pp. 97\u2013104","DOI":"10.1145\/2460239.2460249"},{"issue":"6","key":"7_CR73","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.3390\/su11061596","volume":"11","author":"L Leng","year":"2019","unstructured":"L. Leng, Y. Zhao, Z. Wang, J. Zhang, W. Wang, C. Zhang, A novel hyper-heuristic for the biobjective regional low-carbon location-routing problem with multiple constraints. Sustainability 11(6), 1596 (2019)","journal-title":"Sustainability"},{"key":"7_CR74","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.renene.2016.12.022","volume":"105","author":"W Li","year":"2017","unstructured":"W. Li, E. Ozcan, R. John, Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation. Renew. Energy 105, 473\u2013482 (2017)","journal-title":"Renew. Energy"},{"key":"7_CR75","doi-asserted-by":"crossref","unstructured":"W. Li, E. Ozcan, R. John, A learning automata based multiobjective hyper-heuristic, in IEEE Transactions on Evolutionary Computation (2018)","DOI":"10.1109\/TEVC.2017.2785346"},{"key":"7_CR76","unstructured":"J.A.P. Lima, S.R. Vergilio, et\u00a0al., Automatic generation of search-based algorithms applied to the feature testing of software product lines, in Proceedings of the 31st Brazilian Symposium on Software Engineering (ACM, 2017), pp. 114\u2013123"},{"key":"7_CR77","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.engappai.2018.10.008","volume":"77","author":"J Lin","year":"2019","unstructured":"J. Lin, Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time. Eng. Appl. Artif. Intell. 77, 186\u2013196 (2019)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"7_CR78","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.swevo.2017.04.007","volume":"36","author":"J Lin","year":"2017","unstructured":"J. Lin, Z.-J. Wang, X. Li, A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem. Swarm Evol. Comput. 36, 124\u2013135 (2017)","journal-title":"Swarm Evol. Comput."},{"key":"7_CR79","doi-asserted-by":"crossref","unstructured":"J. Lin, L. Zhu, K. Gao, A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem, in Expert Systems with Applications (2019), pp. 112915","DOI":"10.1016\/j.eswa.2019.112915"},{"key":"7_CR80","doi-asserted-by":"crossref","unstructured":"A. Lissovoi, P.S. Oliveto, J.A. Warwicker, On the time complexity of algorithm selection hyper-heuristics for multimodal optimisation, in Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033 (2019), pp. 2322\u20132329","DOI":"10.1609\/aaai.v33i01.33012322"},{"key":"7_CR81","doi-asserted-by":"crossref","unstructured":"Y. Liu, Y.\u00a0Mei, M. Zhang, Z. Zhang, Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem, in the 18th Annual Conference on Genetic and Evolutionary Computation (GECCO) (Berlin, Germany, 2017)","DOI":"10.1145\/3071178.3071185"},{"key":"7_CR82","unstructured":"M. L\u00f3pez-Ib\u00e1nez, M.-E. Kessaci, T. St\u00fctzle, Automatic design of hybrid metaheuristics from algorithmic components. Technical report (2017)"},{"key":"7_CR83","doi-asserted-by":"crossref","unstructured":"H.R. Louren\u00e7o, O.C. Martin, T. St\u00fctzle, Iterated local search: framework and applications, in Handbook of metaheuristics (Springer, 2019), pp. 129\u2013168","DOI":"10.1007\/978-3-319-91086-4_5"},{"key":"7_CR84","doi-asserted-by":"crossref","unstructured":"N. Louren\u00e7o, F. Pereira, E. Costa, Evolving evolutionary algorithms, in Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation (ACM, 2012), pp. 51\u201358","DOI":"10.1145\/2330784.2330794"},{"key":"7_CR85","unstructured":"S. Luke, Issues in scaling genetic programming: breeding strategies, tree generation, and code bloat. Ph.D. thesis, research directed by Dept. of Computer Science.University of Maryland, College Park (2000)"},{"key":"7_CR86","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.asoc.2014.12.012","volume":"28","author":"M Maashi","year":"2015","unstructured":"M. Maashi, G. Kendall, E. \u00d6zcan, Choice function based hyper-heuristics for multi-objective optimization. Appl. Soft Comput. 28, 312\u2013326 (2015)","journal-title":"Appl. Soft Comput."},{"key":"7_CR87","doi-asserted-by":"crossref","unstructured":"M. Maashi, E. Ozcan, G. Kendall, A multi-objective hyper-heuristic based on choice function. Expert Syst. Appl. 41(9) (2014)","DOI":"10.1016\/j.eswa.2013.12.050"},{"key":"7_CR88","doi-asserted-by":"crossref","unstructured":"T. Mariani, G. Guizzo, S.R. Vergilio, A.T.R. Pozo, Grammatical evolution for the multi-objective integration and test order problem, in Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (ACM, 2016), pp. 1069\u20131076","DOI":"10.1145\/2908812.2908816"},{"key":"7_CR89","doi-asserted-by":"crossref","unstructured":"F. Mascia, M. L\u00f3pez-Ib\u00e1\u00f1ez, J. Dubois-Lacoste, T. St\u00fctzle, Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools. Comput. Oper. Res. (2014), pp. 190\u2013199","DOI":"10.1016\/j.cor.2014.05.020"},{"key":"7_CR90","doi-asserted-by":"crossref","unstructured":"A. Mendes, A.\u00a0Nealen, J.\u00a0Togelius. Hyperheuristic general video game playing, in Proceedings of the IEEE Computational Intelligence and Games (CIG) (2016)","DOI":"10.1109\/CIG.2016.7860398"},{"key":"7_CR91","doi-asserted-by":"crossref","unstructured":"P.B. Miranda, R.B. Prud\u00eancio, GEFPSO: a framework for pso optimization based on grammatical evolution, in Proceedings of the Annual Conference on Genetic and Evolutionary Computation (GECCO) (ACM, 2015), pp. 1087\u20131094","DOI":"10.1145\/2739480.2754819"},{"key":"7_CR92","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.asoc.2017.06.040","volume":"60","author":"PBC Miranda","year":"2017","unstructured":"P.B.C. Miranda, R.B.C. Prudencio, Generation of particle swarm optimization algorithms: an experimental study using grammar-guided genetic programming. Appl. Soft Comput. 60, 281\u2013296 (2017)","journal-title":"Appl. Soft Comput."},{"key":"7_CR93","doi-asserted-by":"crossref","unstructured":"P.B.C. Miranda, R.B.C. Prud\u00eancio, G.L. Pappa, H3AD: a hybrid hyper-heuristic for algorithm design, in Information Sciences (2017)","DOI":"10.1016\/j.ins.2017.05.029"},{"key":"7_CR94","doi-asserted-by":"crossref","unstructured":"M.\u00a0M\u0131s\u0131r, Matrix factorization based benchmark set analysis: a case study on HyFlex, in the 11th International Conference on Simulated Evolution and Learning (SEAL), vol. 10593. LNCS (Springer, 2017), pp. 184\u2013195","DOI":"10.1007\/978-3-319-68759-9_16"},{"key":"7_CR95","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.artint.2016.12.001","volume":"244","author":"M M\u0131s\u0131r","year":"2017","unstructured":"M. M\u0131s\u0131r, M. Sebag, ALORS: an algorithm recommender system. Artif. Intell. 244, 291\u2013314 (2017)","journal-title":"Artif. Intell."},{"key":"7_CR96","doi-asserted-by":"crossref","unstructured":"M.\u00a0M\u0131s\u0131r, K.\u00a0Verbeeck, P.\u00a0De Causmaecker, G.\u00a0Vanden Berghe, Hyper-heuristics with a dynamic heuristic set for the home care scheduling problem, in Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (Barcelona, Spain, 18\u201323 2010), pp. 2875\u20132882","DOI":"10.1109\/CEC.2010.5586348"},{"key":"7_CR97","unstructured":"M.\u00a0M\u0131s\u0131r, T.\u00a0Wauters, K.\u00a0Verbeeck, G.\u00a0Vanden Berghe, A hyper-heuristic with learning automata for the traveling tournament problem, in Metaheuristics: Intelligent Decision Making, the 8th Metaheuristics International Conference (MIC) - Post Conference Volume (Springer, 2011)"},{"key":"7_CR98","doi-asserted-by":"crossref","unstructured":"M. M\u0131s\u0131r, P. Smet, G.V.\u00a0Berghe, An analysis of generalised heuristics for vehicle routing and personnel rostering problems. J. Oper. Res. Soc. 66(5), 858\u2013870 (2015)","DOI":"10.1057\/jors.2014.11"},{"key":"7_CR99","doi-asserted-by":"crossref","unstructured":"M. M\u0131s\u0131r, K. Verbeeck, P. De\u00a0Causmaecker, G.V.\u00a0Berghe, An investigation on the generality level of selection hyper-heuristics under different empirical conditions. Appl. Soft Comput. 13(7), 3335\u20133353 (2013)","DOI":"10.1016\/j.asoc.2013.02.006"},{"key":"7_CR100","doi-asserted-by":"crossref","unstructured":"M. M\u0131s\u0131r, K. Verbeeck, P. De\u00a0Causmaecker, G.V.\u00a0Berghe, A new hyper-heuristic as a general problem solver: an implementation in HyFlex. J. Sched. 16(3), 291\u2013311 (2013)","DOI":"10.1007\/s10951-012-0295-8"},{"key":"7_CR101","unstructured":"A. Mitsos, J. Najman, I.G. Kevrekidis, Optimal deterministic algorithm generation (2016). arXiv:1609.06917"},{"key":"7_CR102","doi-asserted-by":"crossref","unstructured":"S.\u00a0Nguyen, M. Zhang, M. Johnston, K.C. Tan, Automatic programming via iterated local search for dynamic job shop scheduling. IEEE Trans. Cybern. 45(1), 1\u201314 (2015)","DOI":"10.1109\/TCYB.2014.2317488"},{"key":"7_CR103","doi-asserted-by":"crossref","unstructured":"B. Nikpour, H. Nezamabadi-pour, HTSS: a hyper-heuristic training set selection method for imbalanced data sets. Iran J. Comput. Sci. pp. 1\u201320 (2018)","DOI":"10.1007\/s42044-018-0009-2"},{"key":"7_CR104","doi-asserted-by":"crossref","unstructured":"G.\u00a0Ochoa, M.\u00a0Hyde, T.\u00a0Curtois, J.A. Vazquez-Rodriguez, J.\u00a0Walker, M.\u00a0Gendreau, G.\u00a0Kendall, B.\u00a0McCollum, A.J. Parkes, S.\u00a0Petrovic, E.K. Burke, Hyflex: a benchmark framework for cross-domain heuristic search, in European Conference on Evolutionary Computation in Combinatorial Optimisation(EvoCOP), vol. 7245. LNCS (Springer, Berlin, 2012), pp. 136\u2013147","DOI":"10.1007\/978-3-642-29124-1_12"},{"key":"7_CR105","doi-asserted-by":"crossref","unstructured":"G.\u00a0Ochoa, J.\u00a0Walker, M.\u00a0Hyde, T.\u00a0Curtois, Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework, in Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN), vol. 7492. LNCS, ed. by C.A. Coello Coello, V.\u00a0Cutello, K.\u00a0Deb, S.\u00a0Forrest, G.\u00a0Nicosia, M.\u00a0Pavone (Springer, 2012), pp. 418\u2013427","DOI":"10.1007\/978-3-642-32964-7_42"},{"key":"7_CR106","doi-asserted-by":"crossref","unstructured":"M. O\u2019Neil, C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language (Springer, 2003)","DOI":"10.1007\/978-1-4615-0447-4_2"},{"key":"7_CR107","unstructured":"M. O\u2019Neill, A. Brabazon, Grammatical differential evolution, in IC-AI (2006), pp. 231\u2013236"},{"issue":"1","key":"7_CR108","doi-asserted-by":"publisher","first-page":"39","DOI":"10.4018\/jamc.2010102603","volume":"1","author":"E \u00d6zcan","year":"2010","unstructured":"E. \u00d6zcan, M. M\u0131s\u0131r, G. Ochoa, E.K. Burke, A reinforcement learning - great-deluge hyper-heuristic for examination timetabling. Int. J. Appl. Metaheuristic Comput. 1(1), 39\u201359 (2010)","journal-title":"Int. J. Appl. Metaheuristic Comput."},{"key":"7_CR109","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.asoc.2017.11.020","volume":"63","author":"J Park","year":"2018","unstructured":"J. Park, S. Yi Mei, G.C. Nguyen, M. Zhang, An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling. Appl. Soft Comput. 63, 72\u201386 (2018)","journal-title":"Appl. Soft Comput."},{"key":"7_CR110","doi-asserted-by":"crossref","unstructured":"N. Pillay, D. Beckedahl, EvoHyp - a java toolkit for evolutionary algorithm hyper-heuristics, in IEEE Congress on Evolutionary Computation (CEC) (San Sebastian, Spain, 2017)","DOI":"10.1109\/CEC.2017.7969636"},{"issue":"1","key":"7_CR111","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s10479-017-2625-x","volume":"275","author":"N Pillay","year":"2019","unstructured":"N. Pillay, E. \u00d6zcan, Automated generation of constructive ordering heuristics for educational timetabling. Ann. Oper. Res. 275(1), 181\u2013208 (2019)","journal-title":"Ann. Oper. Res."},{"key":"7_CR112","doi-asserted-by":"crossref","unstructured":"N. Pillay, Q. Rong, Hyper-Heuristics: Theory and Applications. Natural Computing Series (Springer, 2018)","DOI":"10.1007\/978-3-319-96514-7"},{"key":"7_CR113","doi-asserted-by":"crossref","unstructured":"N. Pillay, R. Qu, Nurse rostering problems, in Hyper-Heuristics: Theory and Applications (Springer, 2018), pp. 61\u201366","DOI":"10.1007\/978-3-319-96514-7_8"},{"key":"7_CR114","doi-asserted-by":"crossref","unstructured":"E. Pitzer, M. Affenzeller, A comprehensive survey on fitness landscape analysis, in Recent Advances in Intelligent Engineering Systems (Springer, 2012), pp. 161\u2013191","DOI":"10.1007\/978-3-642-23229-9_8"},{"key":"7_CR115","doi-asserted-by":"crossref","unstructured":"R. Poli, M. Graff, There is a free lunch for hyper-heuristics, genetic programming and computer scientists, in the 12th European Conference on Genetic Programming (EuroGP) (Tubingen, Germany, 2009)","DOI":"10.1007\/978-3-642-01181-8_17"},{"key":"7_CR116","doi-asserted-by":"crossref","unstructured":"C.B. Pop, V.R. Chifu, N. Dragoi, I. Salomie, E.S. Chifu, Recommending healthy personalized daily menus\u2013a cuckoo search-based hyper-heuristic approach, in Applied Nature-Inspired Computing: Algorithms and Case Studies (Springer, 2020), pp. 41\u201370","DOI":"10.1007\/978-981-13-9263-4_3"},{"key":"7_CR117","doi-asserted-by":"crossref","unstructured":"S.M. Pour, J.H. Drake, E.K. Burke, A choice function hyper-heuristic framework for the allocation of maintenance tasks in danish railways. Comput. Oper. Res. 93, 15\u201326 (2018)","DOI":"10.1016\/j.cor.2017.09.011"},{"issue":"9","key":"7_CR118","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1057\/jors.2008.102","volume":"60","author":"R Qu","year":"2009","unstructured":"R. Qu, E.K. Burke, Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems. J. Oper. Res. Soc. 60(9), 1273\u20131285 (2009)","journal-title":"J. Oper. Res. Soc."},{"key":"7_CR119","doi-asserted-by":"crossref","unstructured":"Q. Rong, N. Pham, R. Bai, G. Kendall, Hybridising heuristics within an estimation distribution algorithm for examination timetabling. Appl. Intell. 42(4) (2015)","DOI":"10.1007\/s10489-014-0615-0"},{"key":"7_CR120","doi-asserted-by":"crossref","unstructured":"N.R. Sabar, M. Ayob, G. Kendall, R. Qu, Automatic design of a hyper-heuristic framework with gene expression programming for combinatorial optimization problems. IEEE Trans. Evol. Comput. 19(3), 309\u2013325 (2015)","DOI":"10.1109\/TEVC.2014.2319051"},{"key":"7_CR121","doi-asserted-by":"crossref","unstructured":"N.R. Sabar, M. Ayob, G. Kendall, R. Qu, A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems. IEEE Trans. Cybern. 45(2), 217\u2013228 (2015)","DOI":"10.1109\/TCYB.2014.2323936"},{"key":"7_CR122","doi-asserted-by":"crossref","unstructured":"N.R. Sabar, A. Turky, A. Song, A. Sattar, An evolutionary hyper-heuristic to optimise deep belief networks for image reconstruction. Appl. Soft Comput. pp. 105510 (2019)","DOI":"10.1016\/j.asoc.2019.105510"},{"key":"7_CR123","unstructured":"W. Samek, T. Wiegand, K.-R. M\u00fcller, Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models (2017). arXiv:1708.08296"},{"key":"7_CR124","doi-asserted-by":"crossref","unstructured":"W. Shi, X. Song, J. Sun, Automatic heuristic generation with scatter programming to solve the hybrid flow shop problem. Adv. Mech. Eng. 7(2) (2015)","DOI":"10.1155\/2014\/587038"},{"key":"7_CR125","doi-asserted-by":"crossref","unstructured":"K. Sim, E. Hart, A combined generative and selective hyper-heuristic for the vehicle routing problem, in Proceedings of Genetic and Evolutionary Computation Conference (GECCO) (ACM, 2016), pp. 1093\u20131100","DOI":"10.1145\/2908812.2908942"},{"issue":"1","key":"7_CR126","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1162\/EVCO_a_00121","volume":"23","author":"K Sim","year":"2015","unstructured":"K. Sim, E. Hart, B. Paechter, A lifelong learning hyper-heuristic method for bin packing. Evol. Comput. 23(1), 37\u201367 (2015)","journal-title":"Evol. Comput."},{"issue":"1","key":"7_CR127","first-page":"149","volume":"3","author":"ES Sin","year":"2012","unstructured":"E.S. Sin, N.S.M. Kham, Hyper heuristic based on great deluge and its variants for exam timetabling problem. Int. J. Artif. Intell. Appl. 3(1), 149\u2013162 (2012)","journal-title":"Int. J. Artif. Intell. Appl."},{"key":"7_CR128","doi-asserted-by":"crossref","unstructured":"J.A. Soria-Alcaraz, G. Ochoa, M.A. Sotelo-Figeroa, E.K. Burke, A methodology for determining an effective subset of heuristics in selection hyper-heuristics. Eur. J. Oper. Res. 260(3), 972\u2013983 (2017)","DOI":"10.1016\/j.ejor.2017.01.042"},{"key":"7_CR129","doi-asserted-by":"crossref","unstructured":"J.A. Soria-Alcaraz, G. Ochoa, M.A. Sotelo-Figueroa, M. Carpio, H. Puga, Iterated vnd versus hyper-heuristics: Effective and general approaches to course timetabling, in Nature-Inspired Design of Hybrid Intelligent Systems (Springer, 2017), pp. 687\u2013700","DOI":"10.1007\/978-3-319-47054-2_45"},{"key":"7_CR130","doi-asserted-by":"crossref","unstructured":"J.A. Soria-Alcaraz, G. Ochoa, J. Swan, M. Carpio, H. Puga, E.K. Burke, Effective learning hyper-heuristics for the course timetabling problem. Eur. J. Oper. Res. 238(1) (2014)","DOI":"10.1016\/j.ejor.2014.03.046"},{"key":"7_CR131","doi-asserted-by":"crossref","unstructured":"J.A. Soria-Alcaraz, E. \u00d6zcan, J. Swan, G. Kendall, M. Carpio, Iterated local search using an add and delete hyper-heuristic for university course timetabling. Appl. Soft Comput. 40, 581\u2013593 (2016)","DOI":"10.1016\/j.asoc.2015.11.043"},{"key":"7_CR132","doi-asserted-by":"crossref","unstructured":"A. Sosa-Ascencio, G. Ochoa, H. Terashima-Marin, S.E. Conant-Pablos, Grammar-based generation of variable-selection heuristics for constraint satisfaction problems, Genet. Program. Evolvable Mach. 17(2), 119\u2013144 (2016)","DOI":"10.1007\/s10710-015-9249-1"},{"key":"7_CR133","doi-asserted-by":"crossref","unstructured":"M.A. Sotelo-Figueroa, H.J.P. Soberanes, J.M. Carpio, H.J.F. Huacuja, L.C. Reyes, J.A.S. Alcaraz, A. Espinal, Generating bin packing heuristic through grammatical evolution based on bee swarm optimization, in Nature-Inspired Design of Hybrid Intelligent Systems (Springer, 2017), pp. 655\u2013671","DOI":"10.1007\/978-3-319-47054-2_43"},{"key":"7_CR134","doi-asserted-by":"crossref","unstructured":"C. Stone, E. Hart, B. Paechter, Automatic generation of constructive heuristics for multiple types of combinatorial optimisation problems with grammatical evolution and geometric graphs, in International Conference on the Applications of Evolutionary Computation (Springer, 2018), pp. 578\u2013593","DOI":"10.1007\/978-3-319-77538-8_40"},{"key":"7_CR135","doi-asserted-by":"crossref","unstructured":"A. Strickler, J.A.\u00a0Prado Lima, S.R. Vergilio, A.T.R. Pozo, Deriving products for variability test of feature models with a hyper-heuristic approach. Appl. Soft Comput. 49, 1232\u20131242 (2016)","DOI":"10.1016\/j.asoc.2016.07.059"},{"key":"7_CR136","unstructured":"R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (MIT press, 2018)"},{"key":"7_CR137","doi-asserted-by":"crossref","unstructured":"J. Swan, P. De\u00a0Causmaecker, S. Martin, E. Ozcan, A re-characterization of hyper-heuristics, in Recent Developments of Metaheuristics, ed. by L.\u00a0Amodeo, E-G. Talbi, F.\u00a0Yalaoui (Springer, 2018), pp. 75\u201389","DOI":"10.1007\/978-3-319-58253-5_5"},{"key":"7_CR138","doi-asserted-by":"crossref","unstructured":"F. Tao, L. Bi, Y. Zuo, A.Y.C. Nee, Partial\/parallel disassembly sequence planning for complex products. J. Manuf. Sci. Eng. 140(1), 011016 (2018)","DOI":"10.1115\/1.4037608"},{"key":"7_CR139","doi-asserted-by":"crossref","unstructured":"Y. Tenne, C.-K. Goh, Computational Intelligence in Expensive Optimization Problems, vol.\u00a02 (Springer Science & Business Media, 2010)","DOI":"10.1007\/978-3-642-10701-6"},{"key":"7_CR140","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-540-69432-8_4","volume":"54","author":"D Thierens","year":"2007","unstructured":"D. Thierens, Adaptive strategies for operator allocation. Parameter Setting Evol. Algorithms 54, 77\u201390 (2007)","journal-title":"Parameter Setting Evol. Algorithms"},{"key":"7_CR141","doi-asserted-by":"crossref","unstructured":"R.R.S. van Lon, J. Branke, T. Holvoet, Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics, in Genetic Programming and Evolvable Machines (2017), pp. 1\u201328","DOI":"10.1007\/s10710-017-9300-5"},{"key":"7_CR142","doi-asserted-by":"crossref","unstructured":"J. Vanschoren, Meta-learning, in Automated Machine Learning (Springer, 2019), pp. 35\u201361","DOI":"10.1007\/978-3-030-05318-5_2"},{"key":"7_CR143","doi-asserted-by":"crossref","unstructured":"J.D. Walker, G.\u00a0Ochoa, M.\u00a0Gendreau, E.K. Burke, Vehicle routing and adaptive iterated local search within the HyFlex hyper-heuristic framework, in Proceedings of the 6th Learning and Intelligent OptimizatioN Conference (LION), vol. 7219. LNCS, ed. by Y.\u00a0Hamadi, M.\u00a0Schoenauer (Springer, 2012), pp. 265\u2013276","DOI":"10.1007\/978-3-642-34413-8_19"},{"key":"7_CR144","doi-asserted-by":"crossref","unstructured":"D. Whitley, Next generation genetic algorithms: a users guide and tutorial, in Handbook of Metaheuristics (Springer, 2019), pp. 245\u2013274","DOI":"10.1007\/978-3-319-91086-4_8"},{"key":"7_CR145","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67\u201382 (1997)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"11","key":"7_CR146","doi-asserted-by":"publisher","first-page":"10307","DOI":"10.1109\/TVT.2018.2868942","volume":"67","author":"Y Yao","year":"2018","unstructured":"Y. Yao, Z. Peng, B. Xiao, Parallel hyper-heuristic algorithm for multi-objective route planning in a smart city. IEEE Trans. Veh. Technol. 67(11), 10307\u201310318 (2018)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"7_CR147","unstructured":"H. Youssef, E. Monfroy, F. Saubion, Autonomous Search (Springer, New York, 2012)"},{"key":"7_CR148","doi-asserted-by":"crossref","unstructured":"S. Yu, A. Song, A. Aleti, Collective hyper-heuristics for self-assembling robot behaviours, in Pacific Rim International Conference on Artificial Intelligence (Springer, 2018), pp. 499\u2013507","DOI":"10.1007\/978-3-319-97310-4_57"},{"key":"7_CR149","doi-asserted-by":"crossref","unstructured":"S. Yu, A. Song, A. Aleti, A study on online hyper-heuristic learning for swarm robots, in IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2019), pp. 2721\u20132728","DOI":"10.1109\/CEC.2019.8790164"},{"key":"7_CR150","doi-asserted-by":"crossref","unstructured":"K.Z. Zamli, B.Y. Alkazemi, G. Kendall, A tabu search hyper-heuristic strategy for t-way test suite generation. Appl. Soft Comput. 44, 57\u201374 (2016)","DOI":"10.1016\/j.asoc.2016.03.021"},{"issue":"7","key":"7_CR151","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3390\/a12070129","volume":"12","author":"C Zhang","year":"2019","unstructured":"C. Zhang, Y. Zhao, L. Leng, A hyper heuristic algorithm to solve the low-carbon location routing problem. Algorithms 12(7), 129 (2019)","journal-title":"Algorithms"},{"issue":"1","key":"7_CR152","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/3196831","volume":"27","author":"Y Zhang","year":"2018","unstructured":"Y. Zhang, M. Harman, G. Ochoa, G. Ruhe, S. Brinkkemper, An empirical study of meta-and hyper-heuristic search for multi-objective release planning. ACM Trans. Softw. Eng. Methodol. (TOSEM) 27(1), 3 (2018)","journal-title":"ACM Trans. Softw. Eng. Methodol. (TOSEM)"}],"container-title":["Natural Computing Series","Automated Design of Machine Learning and Search Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72069-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T19:38:53Z","timestamp":1672947533000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72069-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720681","9783030720698"],"references-count":152,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72069-8_7","relation":{},"ISSN":["1619-7127"],"issn-type":[{"type":"print","value":"1619-7127"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"29 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}