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Hyper-Heuristic Bibliography Hyper-heuristic Bibliography Mustafa Mısır Department of Computer Engineering, Istinye University, Turkey [email protected] http://mustafamisir.github.io 06/2020 • Web Version: https://mustafamisir.github.io/hh.html • Books: [123] • Surveys: [13, 87, 250, 317, 470, 505, 773, 808] • Tutorials: [32, 468, 725, 857, 868] • Generality: [538] • Theory: [49, 83, 201, 418, 532, 595] • Discussion: [135] [1] Bestoun S Ahmed, Eduard Enoiu, Wasif Afzal, and Kamal Z Zamli. An eval- uation of monte carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. Soft Computing, pages 1{26, 2020. https: //link.springer.com/content/pdf/10.1007/s00500-020-04769-z.pdf. [2] Ayse Aslan, Ilke Bakir, and Iris FA Vis. A dynamic thompson sampling hyper- heuristic framework for learning activity planning in personalized learning. Euro- pean Journal of Operational Research, 2020. https://www.sciencedirect.com/ science/article/pii/S0377221720302526. [3] Miroslaw Blocho. Heuristics, metaheuristics, and hyperheuristics for rich vehicle routing problems. In Smart Delivery Systems, pages 101{156. Elsevier, 2020. https://www.sciencedirect.com/science/article/pii/ B9780128157152000099. 1 [4] Kate M Bowers, Erik M Fredericks, Reihaneh H Hariri, and Betty HC Cheng. Providentia: Using search-based heuristics to optimize satisficement and compet- ing concerns between functional and non-functional objectives in self-adaptive systems. Journal of Systems and Software, 162:110497, 2020. https://www. sciencedirect.com/science/article/pii/S0164121219302717. [5] Mohamed Abd Elaziz, Ahmed A Ewees, and Diego Oliva. Hyper-heuristic method for multilevel thresholding image segmentation. Expert Systems with Applications, page 113201, 2020. https://www.sciencedirect.com/science/article/pii/ S0957417420300270. [6] Isa ahmet Guney, Kemal Poyraz, Gurhan Kucuk, and Ender Ozcan. Hyperheuris- tics for explicit resource partitioning in simultaneous multithreaded processors. Turkish Journal of Electrical Engineering & Computer Sciences, 28(2):821{835, 2020. http://journals.tubitak.gov.tr/elektrik/abstract.htm?id=26731. [7] Abhishek Gupta, HS Bhadauria, and Annapurna Singh. Load balancing based hyper heuristic algorithm for cloud task scheduling. Journal of Ambient Intel- ligence and Humanized Computing, pages 1{8, 2020. https://link.springer. com/content/pdf/10.1007/s12652-020-02127-3.pdf. [8] Xingxing Hao, Rong Qu, and Jing Liu. A unified framework of graph-based evo- lutionary multitasking hyper-heuristic. IEEE Transactions on Evolutionary Com- putation, 2020. https://ieeexplore.ieee.org/abstract/document/9084121/. [9] Olfa Harrabi and Jouhaina Chaouachi. Towards effective resolution approaches for solving the sum coloring problem. Journal of Experimental & Theoretical Arti- ficial Intelligence, 32(1):31{57, 2020. https://www.tandfonline.com/doi/abs/ 10.1080/0952813X.2019.1620869. [10] Ahmed Kheiri. Heuristic sequence selection for inventory routing problem. Trans- portation Science, 54(2):302{312, 2020. https://pubsonline.informs.org/doi/ abs/10.1287/trsc.2019.0934. [11] Mourad Lassouaoui, Dalila Boughaci, and Belaid Benhamou. A synergy thomp- son sampling hyper-heuristic for the feature selection problem. Computational In- telligence, 2020. https://onlinelibrary.wiley.com/doi/abs/10.1111/coin. 12325. [12] Jian Lin, Lei Zhu, and Kaizhou Gao. A genetic programming hyper-heuristic ap- proach for the multi-skill resource constrained project scheduling problem. Expert Systems with Applications, 140:112915, 2020. https://www.sciencedirect.com/ science/article/pii/S0957417419306335. [13] Teodoro Macias-Escobar, Bernabe Dorronsoro, Laura Cruz-Reyes, Nelson Rangel- Valdez, and Claudia Gomez-Santillan. A survey of hyper-heuristics for dy- namic optimization problems. In Intuitionistic and Type-2 Fuzzy Logic En- hancements in Neural and Optimization Algorithms: Theory and Applications, 2 pages 463{477. Springer, 2020. https://link.springer.com/chapter/10.1007/ 978-3-030-35445-9_33. [14] Mitra Montazeri. Hyper-heuristic image enhancement (hhie): A reinforcement learning method for image contrast enhancement. In Advanced Computing and Intelligent Engineering, pages 363{375. Springer, 2020. https://link.springer. com/chapter/10.1007/978-981-15-1081-6_31. [15] H Mosadegh, SMT Fatemi Ghomi, and GA Suer. Stochastic mixed-model as- sembly line sequencing problem: Mathematical modeling and q-learning based simulated annealing hyper-heuristics. European Journal of Operational Research, 282(2):530{544, 2020. https://www.sciencedirect.com/science/article/ pii/S0377221719307611. [16] Luan Carlos Nesi and Rodrigo da Rosa Righi. H2-slan: A hyper-heuristic based on stochastic learning automata network for obtaining, storing, and retrieving heuristic knowledge. Expert Systems with Applications, page 113426, 2020. https: //www.sciencedirect.com/science/article/pii/S0957417420302505. [17] Lale Ozbakir and Gokhan Secme. A hyper-heuristic approach for stochas- tic parallel assembly line balancing problems with equipment costs. Oper- ational Research, 2020. https://link.springer.com/content/pdf/10.1007/ s12351-020-00561-x.pdf. [18] Cristina Bianca Pop, Viorica Rozina Chifu, Nicolae Dragoi, Ioan Salomie, and Emil Stefan Chifu. Recommending healthy personalized daily menus - a cuckoo search-based hyper-heuristic approach. In Applied Nature-Inspired Computing: Algorithms and Case Studies, pages 41{70. Springer, 2020. https://link. springer.com/chapter/10.1007/978-981-13-9263-4_3. [19] Aaron Pope. The automated design of network graph algorithms with applications in cybersecurity, 2020. https://etd.auburn.edu/handle/10415/7076. [20] Yaroslav Pylyavskyy, Ahmed Kheiri, and Leena Ahmed. A reinforcement learning hyper-heuristic for the optimisation of flight connections. In IEEE Congress on Evolutionary Computation (IEEE CEC). IEEE, 2020. https://eprints.lancs. ac.uk/id/eprint/143173/1/CEC2020.pdf. [21] Muhammad Ikram Mohd Rashid, Ahmad Amir Solihin Mohd Apandi, Hamdan Daniyal, and Mohd Ashraf Ahmad. Hyperheuristics trajectory based optimiza- tion for energy management strategy (ems) of split plug-in hybrid electric vehicle. In InECCE2019, pages 837{848. Springer, 2020. https://link.springer.com/ chapter/10.1007/978-981-15-2317-5_69. [22] Jorge A Soria-Alcaraz, Gabriela Ochoa, Andres Espinal, Marco A Sotelo-Figueroa, Manuel Ornelas-Rodriguez, and Horacio Rostro-Gonzalez. A methodology for clas- sifying search operators as intensification or diversification heuristics. Complexity, 2020, 2020. https://www.hindawi.com/journals/complexity/2020/2871835/. 3 [23] Abbas Tarhini, Kassem Danach, and Antoine Harfouche. Swarm intelligence-based hyper-heuristic for the vehicle routing problem with prioritized customers. An- nals of Operations Research, pages 1{22, 2020. https://link.springer.com/ content/pdf/10.1007/s10479-020-03625-5.pdf. [24] Binzi Xu, Yi Mei, Yan Wang, Zhicheng Ji, and Mengjie Zhang. Genetic program- ming with delayed routing for multi-objective dynamic flexible job shop scheduling. Evolutionary Computation, pages 1{31, 2020. https://www.mitpressjournals. org/doi/abs/10.1162/evco_a_00273. [25] WANG Yadong, SHI Quan, XIA Wei, and CHEN Cai. Structure optimization of spare parts supply network based on hyper heuristic algorithm. Journal of Systems Engineering and Electronics, 42(3):620{629, 2020. http://www.jseepub.com/CN/ article/downloadArticleFile.do?attachType=PDF&id=7398. [26] Le Yang, Dakuo He, and Bo Li. A selection hyper-heuristic algorithm for multi- objective dynamic economic and environmental load dispatch. Complexity, 2020, 2020. https://www.hindawi.com/journals/complexity/2020/4939268/abs/. [27] Chunmiao Zhang, Yanwei Zhao, and Longlong Leng. A hyper-heuristic algorithm for time-dependent green location routing problem with time windows. IEEE Access, 2020. https://ieeexplore.ieee.org/abstract/document/9082684/. [28] Man Zhang, Shuming Shi, Yunbo Shen, et al. Self-adaptive hyper-heuristic markov chain evolution for generating vehicle multi-parameter driving cycles. IEEE Transactions on Vehicular Technology, 2020. https://ieeexplore.ieee.org/ abstract/document/9076877/. [29] Shuyan Zhang, Zhilei Ren, Cuixia Li, and Jifeng Xuan. A perturbation adap- tive pursuit strategy based hyper-heuristic for multi-objective optimization prob- lems. Swarm and Evolutionary Computation, 54:100647, 2020. https://www. sciencedirect.com/science/article/pii/S221065021831040X. [30] Yanwei Zhao, Longlong Leng, Jingling Zhang, Chunmiao Zhang, and Wanliang Wang. Evolutionary hyperheuristics for location-routing problem with simulta- neous pickup and delivery. Complexity, 2020, 2020. https://www.hindawi.com/ journals/complexity/2020/9291434/. [31] Leena Ahmed, Christine Mumford, and Ahmed Kheiri. Solving urban transit route design problem using selection hyper-heuristics. European Journal of Operational Research, 274(2):545{559, 2019. The urban transit routing problem (UTRP) fo- cuses on finding efficient travelling routes for vehicles in a public transportation system. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of difficult combinatorial problems, whose optimal solution is hard to find with the complex- ity that arises from the large search space, and the number of constraints imposed in constructing the solution. Hyper-heuristics have emerged as general-purpose 4 search techniques that explore the space of low level heuristics to improve a given solution under an iterative framework. In
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