Automated Design of Production Scheduling Heuristics: A Review

IEEE Transactions on Evolutionary Computation

Volumn 20, Issue 1, 2016, Pages 110-124

  Branke, Jurgen ^a   Nguyen, Su ^b   Pickardt, Christoph W ^a   Zhang, Mengjie ^b  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

evolutionary design; genetic programming; hyper heuristic; scheduling

Indexed keywords

COMPUTER SOFTWARE REUSABILITY; DESIGN; GENETIC ALGORITHMS; GENETIC PROGRAMMING; HEURISTIC METHODS; PRODUCTION CONTROL; STOCHASTIC SYSTEMS;

APPLICATION AREA; AUTOMATED DESIGN; EVOLUTIONARY DESIGN; HYPER-HEURISTICS; HYPERHEURISTIC; PRODUCTION SCHEDULING; STATE-OF-THE-ART APPROACH; STOCHASTIC SCHEDULING;

SCHEDULING;

EID: 84962109552     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2015.2429314     Document Type: Review

References (101)

1. M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems, 3rd ed. New York, NY, USA: Springer, 2008.
2. E. K. Burke et al., "A classification of hyper-heuristic approaches," in Handbook of Metaheuristics (International Series in Operations Research & Management Science), vol. 146, 2nd ed., M. Gendreau and J.-Y. Potvin, Eds. New York, NY, USA: Springer, 2010, pp. 449-468.
3. E. K. Burke et al., "Hyper-heuristics: A survey of the state of the art," J. Oper. Res. Soc., vol. 64, no. 12, pp. 1695-1724, 2013.
4. H. Fisher and G. L. Thompson, "Probabilistic learning combinations of local job-shop scheduling rules," in Industrial Scheduling, J. F. Muth and G. L. Thompson, Eds. Englewood Cliffs, NJ, USA: Prentice-Hall, 1963, pp. 225-251.
5. R. H. Storer, S. D. Wu, and R. Vaccari, "New search spaces for sequencing problems with application to job shop scheduling," Manage. Sci., vol. 38, no. 10, pp. 1495-1509, 1992.
6. U. Dorndorf and E. Pesch, "Evolution based learning in a job shop scheduling environment," Comput. Oper. Res., vol. 22, no. 1, pp. 25-40, 1995.
7. M. J. Shaw, "A pattern-directed approach to flexible manufacturing: A framework for intelligent scheduling, learning, and control," Int. J. Flex. Manuf. Syst., vol. 2, no. 2, pp. 121-144, 1989.
8. S. Nakasuka and T. Yoshida, "Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool," Int. J. Prod. Res., vol. 30, no. 2, pp. 411-431, 1992.
9. W. Mouelhi-Chibani and H. Pierreval, "Training a neural network to select dispatching rules in real time," Comput. Ind. Eng., vol. 58, no. 2, pp. 249-256, 2010.
10. R. Kumar, A. H. Joshi, K. K. Banka, and P. I. Rockett, "Evolution of hyperheuristics for the biobjective 0/1 knapsack problem by multiobjective genetic programming," in Proc. 10th Annu. Conf. Genet. Evol. Comput. (GECCO), Atlanta, GA, USA, 2008, pp. 1227-1234.
11. E. Özcan and A. J. Parkes, "Policy matrix evolution for generation of heuristics," in Proc. 13th Annu. Conf. Genet. Evol. Comput. (GECCO), Dublin, Ireland, 2011, pp. 2011-2018.
12. E. K. Burke, M. R. Hyde, G. Kendall, and J. Woodward, "Automating the packing heuristic design process with genetic programming," Evol. Comput., vol. 20, no. 1, pp. 63-89, 2012.
13. M. Oltean and D. Dumitrescu, "Evolving TSP heuristics using multi expression programming," in Computational Science-ICCS (LNCS 3037), M. Bubak, G. D. van Albada, P. M. A. Sloot, and J. Dongarra, Eds. Berlin, Germany: Springer, 2004, pp. 670-673.
14. A. Beham, M. Kofler, S. Wagner, and M. Affenzeller, "Agent-based simulation of dispatching rules in dynamic pickup and delivery problems," in Proc. 2nd Int. Symp. Logist. Ind. Informat., Linz, Austria, 2009, pp. 1-6.
15. S. Vonolfen, A. Beham, M. Kommenda, and M. Affenzeller, "Structural synthesis of dispatching rules for dynamic dial-a-ride problems," in Computer Aided Systems Theory-EUROCAST (LNCS 8111), R. Moreno-Díaz, F. Pichler, and A. Quesada-Arencibia, Eds. Berlin, Germany: Springer, 2013, pp. 276-283.
16. M. Bader-El-Den, R. Poli, and S. Fatima, "Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework," Memet. Comput., vol. 1, no. 3, pp. 205-219, 2009.
17. N. Pillay, "Evolving hyper-heuristics for the uncapacitated examination timetabling problem," J. Oper. Res. Soc., vol. 63, no. 1, pp. 47-58, 2012.
18. V. H. L. Cheng, L. S. Crawford, and P. K. Menon, "Air traffic control using genetic search techniques," in Proc. IEEE Int. Conf. Control Appl., vol. 1. Kohala Coast, HI, USA, 1999, pp. 249-254.
19. J. V. Hansen, "Genetic search methods in air traffic control," Comput. Oper. Res., vol. 31, no. 3, pp. 445-459, 2004.
20. T. Frankola, M. Golub, and D. Jakobovic, "Evolutionary algorithms for the resource constrained scheduling problem," in Proc. 30th Int. Conf. Inf. Technol. Interf. (ITI), Dubrovnik, Croatia, 2008, pp. 715-722.
21. A. El-Bouri, S. Balakrishnan, and N. Popplewell, "Sequencing jobs on a single machine: A neural network approach," Eur. J. Oper. Res., vol. 126, no. 3, pp. 474-490, 2000.
22. C. Dimopoulos and A. M. S. Zalzala, "Investigating the use of genetic programming for a classic one-machine scheduling problem," Adv. Eng. Softw., vol. 32, no. 6, pp. 489-498, 2001.
23. W.-J. Yin, M. Liu, and C. Wu, "Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming," in Proc. Congr. Evol. Comput. (CEC), vol. 2. Canberra, ACT, Australia, 2003, pp. 1050-1055.
24. X. Li and S. Olafsson, "Discovering dispatching rules using data mining," J. Schedul., vol. 8, no. 6, pp. 515-527, 2005.
25. C. D. Geiger, R. Uzsoy, and H. Aytug, "Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach," J. Schedul., vol. 9, no. 1, pp. 7-34, 2006.
26. D. Jakobovíc and L. Budin, "Dynamic scheduling with genetic programming," in Genetic Programming (LNCS 3905), P. Collet, M. Tomassini, M. Ebner, S. Gustafson, and A. Ekárt, Eds. Berlin, Germany: Springer, 2006, pp. 73-84.
27. C. D. Geiger and R. Uzsoy, "Learning effective dispatching rules for batch processor scheduling," Int. J. Prod. Res., vol. 46, no. 6, pp. 1431-1454, 2008.
28. M. Kofler, S. Wagner, A. Beham, G. Kronberger, and M. Affenzeller, "Priority rule generation with a genetic algorithm to minimize sequence dependent setup costs," in Computer Aided Systems Theory-EUROCAST (LNCS 5717), R. Moreno-Díaz, F. Pichler, and A. Quesada-Arencibia, Eds. Berlin, Germany: Springer, 2009, pp. 817-824.
29. L. Nie, X. Shao, L. Gao, and W. Li, "Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems," Int. J. Adv. Manuf. Technol., vol. 50, nos. 5-8, pp. 729-747, 2010.
30. S. Olafsson and X. Li, "Learning effective new single machine dispatching rules from optimal scheduling data," Int. J. Prod. Econ., vol. 128, no. 1, pp. 118-126, 2010.
31. D. Jakobovíc and K. Marasovíc, "Evolving priority scheduling heuristics with genetic programming," Appl. Soft Comput., vol. 12, no. 9, pp. 2781-2789, 2012.
32. S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan, "Learning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming," in Genetic Programming (LNCS 7831), K. Krawiec, A. Moraglio, T. Hu, A. S. Etaner-Uyar, and B. Hu, Eds. Berlin, Germany: Springer, 2013, pp. 157-168.
33. J. Park, S. Nguyen, M. Zhang, and M. Johnston, "Genetic programming for order acceptance and scheduling," in Proc. IEEE Congr. Evol. Comput. (CEC), Cancún, Mexico, 2013, pp. 1005-1012.
34. D. Jakobovíc, L. Jelenkovíc, and L. Budin, "Genetic programming heuristics for multiple machine scheduling," in Genetic Programming (LNCS 4445), M. Ebner, M. O'Neill, A. Ekárt, L. Vanneschi, and A. I. Esparcia-Alcázar, Eds. Berlin, Germany: Springer, 2007, pp. 321-330.
35. J. C. Hershauer and R. J. Ebert, "Search and simulation selection of a job-shop sequencing rule," Manage. Sci., vol. 21, no. 7, pp. 833-843, 1975.
36. P. J. O'Grady and C. Harrison, "Search-based job shop scheduling and sequencing: Extensions to the search sequencing rule," Math. Comput. Model., vol. 13, no. 3, pp. 13-27, 1990.
37. L. Atlan, J. Bonnet, and M. Naillon, "Learning distributed reactive strategies by genetic programming for the general job shop problem," in Proc. 7th Florida Artif. Intell. Res. Symp., Pensacola, FL, USA, 1994, pp. 1-11.
38. T. Eguchi, F. Oba, and T. Hirai, "A neural network approach to dynamic job shop scheduling," in Global Production Management (IFIP- The International Federation for Information Processing), vol. 24, K. Mertins, O. Krause, and B. Schallock, Eds. New York, NY, USA: Springer, 1999, pp. 152-159.
39. D. A. Koonce and S.-C. Tsai, "Using data mining to find patterns in genetic algorithm solutions to a job shop schedule," Comput. Ind. Eng., vol. 38, no. 3, pp. 361-374, 2000.
40. K. Miyashita, "Job-shop scheduling with genetic programming," in Proc. Genet. Evol. Comput. Conf. (GECCO), Las Vegas, NV, USA, 2000, pp. 505-512.
41. T. Eguchi, F. Oba, and S. Toyooka, "A robust scheduling rule using a neural network in dynamically changing job-shop environments," Int. J. Manuf. Technol. Manage., vol. 14, nos. 3-4, pp. 266-288, 2008.
42. G. R. Weckman, C. V. Ganduri, and D. A. Koonce, "A neural network job-shop scheduler," J. Intell. Manuf., vol. 19, no. 2, pp. 191-201, 2008.
43. M. Kofler, A. Beham, S. Wagner, and M. Affenzeller, "Evaluation of dispatching strategies for the optimization of a real-world production plant," in Proc. 2nd Int. Symp. Logist. Ind. Informat., Linz, Austria, 2009, pp. 25-30.
44. T. Hildebrandt, J. Heger, and B. Scholz-Reiter, "Towards improved dispatching rules for complex shop floor scenarios-A genetic programming approach," in Proc. 12th Annu. Conf. Genet. Evol. Comput. (GECCO), Portland, OR, USA, 2010, pp. 257-264.
45. A. M. Kuczapski, M. V. Micea, L. A. Maniu, and V. I. Cretu, "Efficient generation of near optimal initial populations to enhance genetic algorithms for job-shop scheduling," Inf. Technol. Control, vol. 39, no. 1, pp. 32-37, 2010.
46. H. Ingimundardottir and T. P. Runarsson, "Supervised learning linear priority dispatch rules for job-shop scheduling," in Learning and Intelligent Optimization (LNCS 6683), C. A. C. Coello, Ed. Berlin, Germany: Springer, 2011, pp. 263-277.
47. M. Kapanoglu and M. Alikalfa, "Learning IF-THEN priority rules for dynamic job shops using genetic algorithms," Robot. Comput.-Integr. Manuf., vol. 27, no. 1, pp. 47-55, 2011.
48. L. Nie, L. Gao, P. Li, and L. Zhang, "Application of gene expression programming on dynamic job shop scheduling problem," in Proc. 15th Int. Conf. Comput. Support. Cooper. Work Design, Lausanne, Switzerland, 2011, pp. 291-295.
49. S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan, "A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem," IEEE Trans. Evol. Comput., vol. 17, no. 5, pp. 621-639, Oct. 2013.
50. S. Nguyen, M. Zhang, M. Johnston, and K. Tan, "Learning iterative dispatching rules for job shop scheduling with genetic programming," Int. J. Adv. Manuf. Technol., vol. 67, nos. 1-4, pp. 85-100, 2013.
51. S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan, "Dynamic multiobjective job shop scheduling: A genetic programming approach," in Automated Scheduling and Planning (Studies in Computational Intelligence), vol. 505, A. S. Etaner-Uyar, E. Özcan, and N. Urquhart, Eds. Berlin, Germany: Springer, 2013, pp. 251-282.
52. J. Branke, T. Hildebrandt, and B. Scholz-Reiter, "Hyper-heuristic evolution of dispatching rules: A comparison of rule representations," Evol. Comput., vol. 23, no. 2, pp. 249-277, 2015.
53. T. Hildebrandt and J. Branke, "On using surrogates with genetic programming," Evol. Comput., 2014. DOI: 10.1162/EVCO-a-00133.
54. R. Hunt, M. Johnston, and M. Zhang, "Evolving 'less-myopic' scheduling rules for dynamic job shop scheduling with genetic programming," in Proc. Conf. Genet. Evol. Comput. (GECCO), Vancouver, BC, Canada, 2014, pp. 927-934.
55. R. Hunt, M. Johnston, and M. Zhang, "Evolving machine-specific dispatching rules for a two-machine job shop using genetic programming," in Proc. IEEE Congr. Evol. Comput. (CEC), Beijing, China, 2014, pp. 618-625.
56. S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan, "Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming," IEEE Trans. Evol. Comput., vol. 18, no. 2, pp. 193-208, Apr. 2014.
57. D. H. Baek, W. C. Yoon, and S. C. Park, "A spatial rule adaptation procedure for reliable production control in a wafer fabrication system," Int. J. Prod. Res., vol. 36, no. 6, pp. 1475-1491, 1998.
58. D. H. Baek and W. C. Yoon, "Co-evolutionary genetic algorithm for multi-machine scheduling: Coping with high performance variability," Int. J. Prod. Res., vol. 40, no. 1, pp. 239-254, 2002.
59. H. Tamaki, K. Sakakibara, H. Murao, and S. Kitamura, "Rule acquisition for production scheduling: A genetics-based machine learning approach to flexible shop scheduling," in Proc. Annu. Conf. SICE, vol. 3. Fukui, Japan, 2003, pp. 2762-2767.
60. J. C. Tay and N. B. Ho, "Designing dispatching rules to minimize total tardiness," in Evolutionary Scheduling (Studies in Computational Intelligence), vol. 49, K. P. Dahal, K. C. Tan, and P. I. Cowling, Eds. Berlin, Germany: Springer, 2007, pp. 101-124.
61. A. Beham, S. Winkler, S. Wagner, and M. Affenzeller, "A genetic programming approach to solve scheduling problems with parallel simulation," in Proc. IEEE Int. Parallel Distrib. Process. Symp., Miami, FL, USA, 2008, pp. 1-5.
62. J. C. Tay and N. B. Ho, "Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems," Comput. Ind. Eng., vol. 54, no. 3, pp. 453-473, 2008.
63. E. Pitzer, A. Beham, M. Affenzeller, H. Heiss, and M. Vorderwinkler, "Production fine planning using a solution archive of priority rules," in Proc. 3rd IEEE Int. Symp. Logist. Ind. Informat., Budapest, Hungary, 2011, pp. 111-116.
64. L. Nie, L. Gao, P. Li, and X. Li, "A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates," J. Intell. Manuf., vol. 24, no. 4, pp. 763-774, 2013.
65. C. W. Pickardt, T. Hildebrandt, J. Branke, J. Heger, and B. Scholz-Reiter, "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems," Int. J. Prod. Econ., vol. 145, no. 1, pp. 67-77, 2013.
66. J. A. Vázquez-Rodríguez and G. Ochoa, "On the automatic discovery of variants of the NEH procedure for flow shop scheduling using genetic programming," J. Oper. Res. Soc., vol. 62, no. 2, pp. 381-396, 2011.
67. F. Mascia, M. López-Ibáñez, J. Dubois-Lacoste, and T. Stützle, "From grammars to parameters: Automatic iterated greedy design for the permutation flow-shop problem with weighted tardiness," in Learning and Intelligent Optimization (LNCS 7997), G. Nicosia and P. Pardalos, Eds. Berlin, Germany: Springer, 2013, pp. 321-334.
68. M. R. Garey, D. S. Johnson, and R. Sethi, "The complexity of flowshop and jobshop scheduling," Math. Oper. Res., vol. 1, no. 2, pp. 117-129, 1976.
69. H. Aytug, M. A. Lawley, K. McKay, S. Mohan, and R. Uzsoy, "Executing production schedules in the face of uncertainties: A review and some future directions," Eur. J. Oper. Res., vol. 161, no. 1, pp. 86-110, 2005.
70. M. E. Pfund, S. J. Mason, and J. W. Fowler, "Semiconductor manufacturing scheduling and dispatching: State of the art and survey of needs," in Handbook of Production Scheduling (International Series in Operations Research & Management Science), vol. 89, J. W. Herrmann, Ed. New York, NY, USA: Springer, 2006, pp. 213-241.
71. V. Sels, N. Gheysen, and M. Vanhoucke, "A comparison of priority rules for the job shop scheduling problem under different flow timeand tardiness-related objective functions," Int. J. Prod. Res., vol. 50, no. 15, pp. 4255-4270, 2011.
72. A. El-Bouri, "A cooperative dispatching approach for minimizing mean tardiness in a dynamic flowshop," Comput. Oper. Res., vol. 39, no. 7, pp. 1305-1314, 2012.
73. A. Otto and C. Otto, "How to design effective priority rules: Example of simple assembly line balancing," Comput. Ind. Eng., vol. 69, pp. 43-52, Mar. 2014.
74. M. Nawaz, E. E. Enscore, Jr., and I. Ham, "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega Int. J. Manage. Sci., vol. 11, no. 1, pp. 91-95, 1983.
75. E. J. Anderson and J. C. Nyirenda, "Two new rules to minimize tardiness in a job shop," Int. J. Prod. Res., vol. 28, no. 12, pp. 2277-2292, 1990.
76. E. Kutanoglu and I. Sabuncuoglu, "An analysis of heuristics in a dynamic job shop with weighted tardiness objectives," Int. J. Prod. Res., vol. 37, no. 1, pp. 165-187, 1999.
77. J. J. Kanet and X. Li, "A weighted modified due date rule for sequencing to minimize weighted tardiness," J. Schedul., vol. 7, no. 4, pp. 261-276, 2004.
78. B. Giffler and G. L. Thompson, "Algorithms for solving productionscheduling problems," Oper. Res., vol. 8, no. 4, pp. 487-503, 1960.
79. E. K. Burke, M. Hyde, G. Kendall, and J. Woodward, "Automatic heuristic generation with genetic programming: Evolving a jack-ofall-trades or a master of one," in Proc. 9th Annu. Conf. Genet. Evol. Comput. (GECCO), London, U.K., 2007, pp. 1559-1565.
80. R. T. Barrett and S. Barman, "A SLAM II simulation study of a simplified flow shop," Simulation, vol. 47, no. 5, pp. 181-189, 1986.
81. F. Mahmoodi, C. T. Mosier, and R. E. Guerin, "The effect of combining simple priority heuristics in flow-dominant shops," Int. J. Prod. Res., vol. 34, no. 3, pp. 819-839, 1996.
82. H. Sarper and M. C. Henry, "Combinatorial evaluation of six dispatching rules in a dynamic two-machine flow shop," Omega Int. J. Manage. Sci., vol. 24, no. 1, pp. 73-81, 1996.
83. S. Barman, "Simple priority rule combinations: An approach to improve both flow time and tardiness," Int. J. Prod. Res., vol. 35, no. 10, pp. 2857-2870, 1997.
84. N. Raman, F. B. Talbot, and R. V. Rachamadugu, "Due date based scheduling in a general flexible manufacturing system," J. Oper. Manage., vol. 8, no. 2, pp. 115-132, 1989.
85. R. A. Ruben and F. Mahmoodi, "Lot splitting in unbalanced production systems," Decis. Sci., vol. 29, no. 4, pp. 921-949, 1998.
86. J. A. C. Bokhorst, G. Nomden, and J. Slomp, "Performance evaluation of family-based dispatching in small manufacturing cells," Int. J. Prod. Res., vol. 46, no. 22, pp. 6305-6321, 2008.
87. N. Ishii and J. J. Talavage, "A mixed dispatching rule approach in FMS scheduling," Int. J. Flex. Manuf. Syst., vol. 6, no. 1, pp. 69-87, 1994.
88. T. Yang, Y. Kuo, and C. Cho, "A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem," Eur. J. Oper. Res., vol. 176, no. 3, pp. 1859-1873, 2007.
89. P. Wong and M. Zhang, "Algebraic simplification of GP programs during evolution," in Proc. 8th Annu. Conf. Genet. Evol. Comput. (GECCO), Seattle, WA, USA, 2006, pp. 927-934.
90. D. Kinzett, M. Johnston, and M. Zhang, "Numerical simplification for bloat control and analysis of building blocks in genetic programming," Evol. Intell., vol. 2, no. 4, pp. 151-168, 2009.
91. M. Johnston, T. Liddle, and M. Zhang, "A relaxed approach to simplification in genetic programming," in Genetic Programming (LNCS 6021), A. I. Esparcia-Alcázar, A. Ekárt, S. Silva, S. Dignum, and A. S. Uyar, Eds. Berlin, Germany: Springer, 2010, pp. 110-121.
92. J. Branke and C. W. Pickardt, "Evolutionary search for difficult problem instances to support the design of job shop dispatching rules," Eur. J. Oper. Res., vol. 212, no. 1, pp. 22-32, 2011.
93. S. Nguyen, M. Zhang, and M. Johnston, "A sequential genetic programming method to learn forward construction heuristics for order acceptance and scheduling," in Proc. IEEE Congr. Evol. Comput. (CEC), Beijing, China, 2014, pp. 1824-1831.
94. J. E. Beasley, "OR-Library: Distributing test problems by electronic mail," J. Oper. Res. Soc., vol. 41, no. 11, pp. 1069-1072, 1990.
95. C. Rajendran and O. Holthaus, "A comparative study of dispatching rules in dynamic flowshops and jobshops," Eur. J. Oper. Res., vol. 116, no. 1, pp. 156-170, 1999.
96. O. Holthaus and C. Rajendran, "Efficient jobshop dispatching rules: Further developments," Prod. Plan. Control, vol. 11, no. 2, pp. 171-178, 2000.
97. T. Hildebrandt. (Sep. 12, 2014). Jasima-An Efficient Java Simulator for Manufacturing and Logistics. [Online]. Available: http://code.google.com/p/jasima/
98. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, USA: MIT Press, 1992.
99. S. Luke and L. Panait, "A comparison of bloat control methods for genetic programming," Evol. Comput., vol. 14, no. 3, pp. 309-344, 2006.
100. P. A. Whigham and G. Dick, "Implicitly controlling bloat in genetic programming," IEEE Trans. Evol. Comput., vol. 14, no. 2, pp. 173-190, Apr. 2010.
101. M. Samorani and M. Laguna, "Data-mining-driven neighborhood search," INFORMS J. Comput., vol. 24, no. 2, pp. 210-227, 2012.