References
- Ahmed, B., Enolu, E., Afzal, W. & Zamli. (2020). An evaluation of monte carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. Soft Computing, https://doi.org/https://doi.org/10.1007/s00500-020-04769-z.
- Bader-El-Den, M., & Poli, R. (2008). Generating SAT local-search heuristics using a GP hyper-heuristic framework [Paper presentation]. Artificial Evolution: International Conference on Artificial Evolution (pp. 37–49). Springer.
- Bader-El-Den, M., Poli, R., & Fatima, S. (2009). Evolving timetabling heuristics using grammar-based genetic programming hyper-heuristic framework. Memetic Computing, 1(3), 205–219. https://doi.org/https://doi.org/10.1007/s12293-009-0022-y
- Burke, E. K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Qu, R. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724. https://doi.org/https://doi.org/10.1057/jors.2013.71
- Burke, E. K., Hyde, M., Kendall, G., & Woodward, J. (2010). A genetic programming hyper-heuristic approach for evolving two dimensional strip packing heuristics. IEEE Transactions on Evolutionary Computation, 14(6), 942–958. https://doi.org/https://doi.org/10.1109/TEVC.2010.2041061
- Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Ender, Ö., & R, W. J. (2019). A classifiction of hyper-heuristic approaches: Revisted. In Handbook of metaheuristics. Springer.
- Burke, E. K., Kendall, G., & Soubeiga, E. (2003). A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6), 451–470. https://doi.org/https://doi.org/10.1023/B:HEUR.0000012446.94732.b6
- Burke, E. K., Kendall, G., Misir, M., & Özcan, E. (2012). Monte carlo hype-heuristics for examination. Annals of Operations Research, 196(1), 73–90. https://doi.org/https://doi.org/10.1007/s10479-010-0782-2
- Burke, E. K., McCollum, B., Meisels, A., Petrovic, S., & Qu, R. (2007). A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research, 176(1), 177–192. https://doi.org/https://doi.org/10.1016/j.ejor.2005.08.012
- Burke, E. K., Petrovic, S., & Qu, R. (2006). Case-based heuristic selection for timetabling problems. Journal of Scheduling, 9(2), 115–132. https://doi.org/https://doi.org/10.1007/s10951-006-6775-y
- Caramia, M., Olmo, P. D., & Italiano, G. (2001). New algorithms for examination timetabling. In S. Naher & D. Wagner (Eds.), 4th international workshop on algorithm engineering (WAE 2000), Lecture Notes in Computer Science (Vol. 1982, pp. 230–241). Springer.
- Chan, C., Xue, F., Ip, W., & Cheung, C. (2012). A hyper-heuristic inspired by pearl hunting. In Y. Hamadi & M. Schoenauer (Eds.), Learning and intelligent optimization, Lecture Notes in Computer Science (Vol. 7219, pp. 349–353). Springer.
- Chen, P.-C., Kendall, G., & Berghe, G. V. (2007). An ant based hyper-heuristic for the travelling tournament problem [Paper presentation]. IEEE symposium on computational intelligence in scheduling (scis ’07) (p. https://doi.org/https://doi.org/10.1109/SCIS.2007.367665)
- Cichowicz, T., Drozdowski, M., Frankiewicz, M., Grzegorz, Rytwiń, F., & Wasilewski, J. (2012). Five phase genetic hive hyper-heuristics for the cross-domain search. In Y. Hamadi & M. Schoenauer (Eds.), Learning and intelligent optimization, Lecture Notes in Computer Science (Vol. 7219, pp. 354–439). Springer.
- Drake, J. H., Hyde, M., Ibrahim, K., & Özcan, E. (2014). A genetic programming hyper-heuristic for the multidimensional knapsack problem. Kybernetes, 43(9/10), 1500–1511. https://doi.org/https://doi.org/10.1108/K-09-2013-0201
- Epitropakis, M., & Burke, E. K. (2018). Hyper-heuristics. In Handbook of metaheuristics. Springer.
- Falkenauer, E. (1996). A hybrid grouping genetic algorithm for bin packing. Journal of Heuristics, 2(1), 5–30. https://doi.org/https://doi.org/10.1007/BF00226291
- Ferreira, A. S., Pozo, A. T. R., & Goncalves, R. A. (2015). An ant colony based hyper-heuristic approach for the set covering problem. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 4(1), 1. https://doi.org/https://doi.org/10.14201/ADCAIJ201541121
- Freedman, D., Pisani, R., & Purves, R. (2007). Statistics: Fourth international student edition. W.W. Norton and Company.
- Fukunaga, A. S. (2008). Automated discovery of local search heuristics for satisfiability testing. Evolutionary Computation, 16(1), 31–61. https://doi.org/https://doi.org/10.1162/evco.2008.16.1.31
- Gaspero, L. D., & Schaerf, A. (2000). Tabu search techniques for examination timetabling. In E. K. Burke & W. Erben (Eds.), Selected papers from the 3rd international conference on the practice and theory of automated timetabling, Lecture Notes in Computer Science (Vol. 2079, pp. 104–117). Springer.
- Guantara, N. (2018). A review of multi-objective optimization: Methods and applications. Electrical and Electronic Engineering, 5.doi:https://doi.org/10.1080/2331916.2018
- Jia, Y., Cohen, M., Harman, M., & Petke, J. (2015). Learning combinatorial interaction test generation strategies using hyperheuristic search. In Proceedings of the 37th international conference on software engineering (ICSE 2015) (Vol. 1, p. 540–550).
- Kendall, G., & Hussin, N. M. (2005). An investigation of a tabu-search-based hyper-heuristic for examination timetabling. In E. Burke, S. Petrovic, & M. Gendreau (Eds.), Multidisciplinary scheduling: Theory and applications (pp. 309–328). Springer.
- Koza, J. (1992). Genetic programming: On the programming of computers by means of natural selection (1st ed.). MIT.
- Kumari, A. C., & Srinivas, K. (2016). Hyper-heuristic approach for multi-objective software module clustering. Journal of Systems and Software, 117, 384–401. https://doi.org/https://doi.org/10.1016/j.jss.2016.04.007
- Lehrbaum, A. (2011). A new hyper-heuristic algorithm for cross-domain search problems [Unpublished master’s thesis]. Faculty of Informatics, Vienna University of Technology.
- Lopez-Camacho, E., Terashima-Marin, H., Ross, P., & Ochoa, G. (2014). A unified hyper-heuristic framework for solving bin packing problems. Expert Systems with Applications, 41(15), 6876–6889. https://doi.org/https://doi.org/10.1016/j.eswa.2014.04.043
- Mausser, H. (2006). Normalization and other topics in multi-objective optimization. In Proceedings of the Field-Mitacs Industrial Problems Workshop (p. 89–101).
- McCollum, B., Schaerf, A., Paechter, B., McMullan, P., Lewis, R., Parkes, A. J., Gaspero, L. D., Qu, R., & Burke, E. K. (2010). Setting the research agenda in automated timetabling: The second international timetabling competition. INFORMS Journal on Computing, 22(1), 120–130. https://doi.org/https://doi.org/10.1287/ijoc.1090.0320
- McKay, R. I., Hoai, N. X., Whigham, P. A., Shan, Y., & O’Neill, M. (2010). Grammar-based genetic programming: A survey. Genetic Programming and Evolvable Machines, 11(3–4), 365–396. https://doi.org/https://doi.org/10.1007/s10710-010-9109-y
- Misir, M., Verbeeck, K., Causmaecker, P. D., & Berghe, G. V. (2013). A new hyper-heuristic as a general problem solver: An implementation in hyflex. Journal of Scheduling, 16(3), 291–311. https://doi.org/https://doi.org/10.1007/s10951-012-0295-8
- Mısır, M., Verbeeck, K., De Causmaecker, P., Vanden Berghe, G. (2013). An investigation on the generality level of selection hyper-heuiristics under different empirical conditions. Applied Soft Computing, 13(7), 3335–3353. https://doi.org/https://doi.org/10.1016/j.asoc.2013.02.006
- O’Neill, M., & Ryan, C. (2003). Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Springer.
- Ochoa, G., Hyde, M., Curtois, T., Vazquez-Rodriguez, J., Walker, J., Gendreau, M., & Burke, E. (2012). Hyflex: A benchmark framework for cross-domain heuristic search. In Evolutionary computation in combinatorial optimization (EVOCOP 2012), Lecture Notes in Computer Science (Vol. 7245, pp. 136–147). Springer.
- Özcan, E., Misir, M., Ochoa, G., & Burke, E. K. (2010). A reinforcement learning-great-deluge hyper-heuristic for examination timetabling. International Journal of Applied Metaheuristic Computing, 1(1), 39–59.
- Pillay, N. (2010). Evolving hyper-heuristics for a highly constrained examination timetabling problem. In Proceedings of the International Conference on the Practice and Theory of Automated Timetabling (PATAT 2010) (p. 336–346).
- Pillay, N. (2012a). Evolving hyper-heuristics for the uncapacitated examination timetabling problem. Journal of the Operational Research Society, 63(1), 47–58. (https://doi.org/https://doi.org/10.1057/jors.2011.12
- Pillay, N. (2012b). A study of evolutionary algorithm hyper-heuristics for the one-dimensional bin-packing problem. South African Computer Journal, 48, 31–40. https://doi.org/https://doi.org/10.18489/sacj.v48i1.87
- Pillay, N. (2016). Evolving construction heuristics for the curriculum based university course timetabling problem. In Y.-S. Ong (Ed.), Proceedings of the IEEE 2016 congress on evolutionary computation (cec 2016) (pp. 4437–4443). IEEE.
- Pillay, N., & Özcan, E. (2019). Automated generation of constructive ordering heuristics for education timetabling. Annals of Operations Research, 275(1), 181–128. https://doi.org/https://doi.org/10.1007/s10479-017-2625-x
- Pillay, N., & Qu, R. (2018). Hyper-heuristics: Theory and applications. Springer.
- Qu, R., & Burke, E. K. (2009). Hybridisations within a graph based hyper-heuristic framework for university timetabling problems. Journal of the Operational Research Society, 60(9), 1273–1285. https://doi.org/https://doi.org/10.1057/jors.2008.102
- Qu, R., Burke, E. K., & McCollum, B. (2009). Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems. European Journal of Operational Research, 198(2), 392–404. https://doi.org/https://doi.org/10.1016/j.ejor.2008.10.001
- Qu, R., Burke, E., McCollum, B., Merlot, L., & Lee, S. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12(1), 55–89. https://doi.org/https://doi.org/10.1007/s10951-008-0077-5
- Raghavjee, R., & Pillay, N. (2015). A selection perturbative hyper-heuristic for solving the school timetabling problem. ORiON, 31(1), 39–60. https://doi.org/https://doi.org/10.5784/31-1-158
- Reinelt, G. (1991). Tsplib-a traveling salesman problem library. ORSA Journal on Computing, 3(4), 376–384. https://doi.org/https://doi.org/10.1287/ijoc.3.4.376
- Ross, P., Schulenburg, S., Marin-Blazquez, J. G., & Hart, E. (2002). Hyper-heuristics: Learning to combine simple heuristics in bin-packing problems. In Proceedings of the genetic and evolutionary computation conference (GECCO 2002) (pp. 942–948). Morgan Kaufman Publishers.
- Ross, P. (2014). Hyper-heuristics. In Search methodologies (pp. 611–638). Springer.
- Sabar, N. R., Ayob, M., Kendall, G., & Qu, R. (2013). Grammatical evolution hyper-heuristic for combinatorial optimization problems. IEEE Transactions on Evolutionary Computation, 17(6), 840–861. https://doi.org/https://doi.org/10.1109/TEVC.2013.2281527
- Sabar, N. R., Ayob, M., Kendall, G., & Qu, R. (2014). A dynamic multi-armed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems. IEEE Transactions on Cybernetics, 45(2), 217–228. https://doi.org/https://doi.org/10.1109/TCYB.2014.2323936
- Sabar, N. R., Ayob, M., Qu, R., & Kendall, G. (2012). A graph colouring constructive hyper-heuristic for examination timetabling problems. Applied Intelligence, 37(1), 1–11. https://doi.org/https://doi.org/10.1007/s10489-011-0309-9
- Scholl, A., Klein, R., & Jurgens, C. (1997). Bison: A fast hybrid procedure for exactly solving the one-dimensional bin packing problem. Computers and Operations Research, 24(7), 626–645. https://doi.org/https://doi.org/10.1016/S0305-0548(96)00082-2
- Sim, K., & Hart, E. (2013). Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model [Paper presentation]. Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (pp. 1549–1556). https://doi.org/https://doi.org/10.1145/2463372.2463555
- Sim, K., & Hart, E. (2016). A combined generative and selective hyper-heuristic for the vehicle routing problem [Paper presentation]. Proceedings of the Genetic and Evolutionary Computation Conference (Gecco ’16) (pp. 1093–1100). ACM. https://doi.org/https://doi.org/10.1145/2908812.2908942
- Sim, K., Hart, E., & Paechter, B. (2015). A lifelong learning hyper-heuristic method for bin packing. Evolutionary Computation, 23(1), 37–67. https://doi.org/https://doi.org/10.1162/EVCO_a_00121
- Sosa-Ascencio, A., Ochoa, G., Terashima-Marin, H., & Conant-Pablos, S. E. (2016). Grammar-based generation of variable-selection heuristics for constraint satisfaction problems. Genetic Programming and Evolvable Machines, 17(2), 119–144. https://doi.org/https://doi.org/10.1007/s10710-015-9249-1
- Terashima-Marín, H., Ross, P., Farías-Zárate, C. J., López-Camacho, E., & Valenzuela-Rendón, M. (2010). Generalized hyper-heuristics for solving 2D regular and irregular packing problems. Annals of Operations Research, 179(1), 369–392. https://doi.org/https://doi.org/10.1007/s10479-008-0475-2
- Terashima-Marín, H., Zarate, C. F., Ross, P., & Valenzuela-Rendon, M. (2006). A GA-based methd to produce generalized hyper-heuristics for the 2D-regular cutting stock problem [Paper presentation]. Proceedings of the 8th Annual Conference on Genetic Programming and Evolutionary Algorithms (pp. 591–598). ACM. https://doi.org/https://doi.org/10.1145/1143997.1144102
- Zamli, K. Z., Alkazemi, B. Y., & Kendall, G. (2016). A Tabu Search hyper-heuristic strategy for t-way test suite generation. Applied Soft Computing, 44, 57–74. https://doi.org/https://doi.org/10.1016/j.asoc.2016.03.021