Hyper-heuristics with low level parameter adaptation

Evolutionary Computation

Volumn 20, Issue 2, 2012, Pages 189-227

  Ren, Zhilei ^a   Jiang, He ^a   Xuan, Jifeng ^a   Luo, Zhongxuan ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Ant colony optimization; Diversification; Heuristic space reduction; Hyper heuristics; Intensification; Parameter control

Indexed keywords

ANT COLONY OPTIMIZATION (ACO); DIVERSIFICATION; HYPER-HEURISTICS; INTENSIFICATION; PARAMETER CONTROL; SPACE REDUCTIONS;

ALGORITHMS;

ALGORITHM; ARTICLE; SEARCH ENGINE; THEORETICAL MODEL;

ALGORITHMS; MODELS, THEORETICAL; SEARCH ENGINE;

EID: 84860802108     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/EVCO_a_00063     Document Type: Article

References (70)

1. Aarts, E. H. L., and Lenstra, J. K. (1997). Local search in combinatorial optimization. New York: Wiley.
2. Beasley, J. E. (1985). A note on solving large p-median problems. European Journal of Operational Research, 21(2):270-273.
3. Bilchev, G., and Parmee, I. (1995). The ant colony metaphor for searching continuous design spaces. In T. Fogarty (Ed.), Evolutionary computing. Lecture Notes in Computer Science, Vol. 993 (pp. 25-39). Berlin: Springer.
4. Birattari, M., Yuan, Z., Balaprakash, P., and St ützle, T. (2010). F-Race and iterated F-Race: An overview. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss (Eds.), Experimental methods for the analysis of optimization algorithms (pp. 311-336). Berlin: Springer.
5. Blum, C., and Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3):268-308.
6. Box, G., and Muller, M. (1958). A note on the generation of random normal deviates. The Annals of Mathematical Statistics, 29(2):610-611.
7. Burke, E. K., Curtois, T., Hyde, M., Kendall, G., Ochoa, G., Petrovic, S., and Vázquez-Rodríguez, J. A. (2009). HyFlex: A flexible framework for the design and analysis of hyper-heuristics. In Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications, pp. 790-797.
8. Burke, E. K., Curtois, T., Kendall, G., Hyde, M., Ochoa, G., and Vázquez-Rodríguez, J. A. (2009). Towards the decathlon challenge of search heuristics. In Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2205-2208.
9. Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., and Woodward, J. R. (2010). A classification of hyper-heuristic approaches. In M. Gendreau and J.-Y. Potvin (Eds.), Handbook of metaheuristics. International Series in Operations Research & Management Science, Vol. 146 (pp. 449-468). Berlin: Springer.
10. Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., and Qu, R. (2010). Hyper-heuristics: A survey of the state of the art. Technical Report No. NOTTCS-TR-SUB-0906241418-2747, School of Computer Science and Information Technology, University of Nottingham.
11. Burke, E. K., Hyde, M., Kendall, G., and Woodward, J. R. (2010). A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. IEEE Transactions on Evolutionary Computation, 14(6):942-958.
12. Burke, E. K., Hyde, M., Kendall, G., and Woodward, J. R. (2012). Automating the packing heuristic design process with genetic programming. Evolutionary Computation, 20(1).
13. Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Özcan, E., and Woodward, J. R. (2009). Exploring hyper-heuristic methodologies with genetic programming. In Computational intelligence. Intelligent Systems Reference Library, Vol. 1 (pp. 177-201). Berlin: Springer.
14. Burke, E. K., Kendall, G., Landa Silva, D., O'Brien, R., and Soubeiga, E. (2005). An ant algorithm hyperheuristic for the project presentation scheduling problem. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), pp. 2263-2270.
15. Burke, E. K., Kendall, G., Newall, J., Hart, E., Ross, P., and Schulenburg, S. (2003). Hyper-heuristics: An emerging direction in modern search technology. In F. Glover and G. Kochenberger (Eds.), Handbook of metaheuristics. International Series in Operations Research & Management Science, Vol. 57 (pp. 457-474). Berlin: Springer.
16. Burke, E. K., Kendall, G., and Soubeiga, E. (2003). A Tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6):451-470.
17. Chen, P.-C., Kendall, G., and Berghe, G. (2007). An ant based hyper-heuristic for the travelling tournament problem. In Proceedings of the IEEE Symposium on Computational Intelligence in Scheduling (SCIS 2007), pp. 19-26.
18. Correa, E., Steiner, M., Freitas, A., and Carnieri, C. (2001). A genetic algorithm for the p-median problem. In Proceedings of the 2001 Genetic and Evolutionary Computation Conference (GECCO'01), pp. 1268-1275.
19. Cowling, P., Kendall, G., and Han, L. (2002). An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In Proceedings of the IEEE Congress on Evolutionary Computation 2002 (CEC 2002), pp. 1185-1190.
20. Cowling, P., Kendall, G., and Soubeiga, E. (2001a). A hyperheuristic approach to scheduling a sales summit. In Practice and Theory of Automated Timetabling III. Lecture Notes in Computer Science, Vol. 2079 (pp. 176-190). Berlin: Springer.
21. Cowling, P., Kendall, G., and Soubeiga, E. (2001b). A parameter-free hyperheuristic for scheduling a sales summit. In Proceedings of the 4th Metaheuristic International Conference (MIC 2001), pp. 127-131.
22. Crawford, K. D., Hoelting, C. J., Wainwright, R. L., and Schoenefeld, D. A. (1997). A study of fixed length subset recombination. In Proceedings of the Fourth Foundations of Genetic Algorithms Workshop, pp. 365-378.
23. Cuesta-Ca ñada, A., Garrido, L., and Terashima-Marín, H. (2005). Building hyper-heuristics through ant colony optimization for the 2D bin packing problem. In Knowledge-based intelligent information and engineering systems, Vol. 3684 (pp. 907-907). Berlin: Springer.
24. DaCosta, L., Fialho, Á., Schoenauer, M., and Sebag, M. (2008). Adaptive operator selection with dynamic multi-armed bandits. In Proceedings of the 2008 Conference on Genetic and Evolutionary Computation (GECCO'08), pp. 913-920.
25. Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1):29-41.
26. Dowsland, K., Soubeiga, E., and Burke, E. (2007). A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation. European Journal of Operational Research, 179(3):759-774.
27. Eiben, A., Michalewicz, Z., Schoenauer, M., and Smith, J. (2007). Parameter control in evolutionary algorithms. In F. Lobo, C. Lima, and Z. Michalewicz (Eds.), Parameter setting in evolutionary algorithms. Studies in Computational Intelligence, Vol. 54 (pp. 19-46). Berlin: Springer.
28. Feo, T. A., and Resende, M. G. C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6(2):109-133.
29. Fukunaga, A. S. (2008). Automated discovery of local search heuristics for satisfiability testing. Evolutionary Computation, 16(1):31-61.
30. García, S., Molina, D., Lozano, M., and Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: A case study on the CEC'2005 Special Session on Real Parameter Optimization. Journal of Heuristics, 15(6):617-644.
31. Han, L., Kendall, G., and Cowling, P. (2002). An adaptive length chromosome hyperheuristic genetic algorithm for a trainer scheduling problem. In Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002), pp. 267-271.
32. Hansen, N. (2006). An analysis of mutative σ-self-adaptation on linear fitness functions. Evolutionary Computation, 14(3):255-275.
33. Hansen, P., and Mladenović, N. (1997). Variable neighborhood search for the p-median. Location Science, 5(4):207-226.
34. Hansen, P., and Mladenović, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130(3):449-467.
35. Ho, N. B., and Tay, J. C. (2005). Evolving dispatching rules for solving the flexible job-shop problem. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), pp. 2848-2855.
36. Holland, J. H. (1992). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press.
37. Hoos, H. H., and St ützle, T. (2005). Stochastic local search: Foundations and applications. San Mateo, CA: Morgan Kaufmann.
38. Hutter, F., Hoos, H. H., Leyton-Brown, K., and St ützle, T. (2009). ParamILS: An automatic algorithm configuration framework. Journal of Artificial Intelligence Research, 36(1):267-306.
39. Kariv, O., and Hakimi, S. (1979). An algorithmic approach to network location problems. II: The p-medians. SIAM Journal on Applied Mathematics, 37(3):539-560.
40. Kendall, G., Soubeiga, E., and Cowling, P. (2002). Choice function and random hyperheuristics. In Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning, SEAL.
41. Kochetov, Y., Levanova, T., Alekseeva, E., and Loresh, M. (2005). Large neighborhood local search for the p-median problem. Yugoslav Journal of Operations Research, 15(1):53-63.
42. Li, Y., and Li, W. (2007). Adaptive ant colony optimization algorithm based on information entropy: Foundation and application. Fundamenta Informaticae, 77(3):229-242.
43. López-Ibáñez M., Dubois-Lacoste J., Stützle T. and Birattari M. (2011). The irace package, Iterated race for automatic algorithm configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium.
44. Meignan, D., Koukam, A., and Créput, J.-C. (2010). Coalition-based metaheuristic: A self-adaptive metaheuristic using reinforcement learning and mimetism. Journal of Heuristics, 16(6):859-879.
45. Merkle, D., Middendorf, M., and Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6(4):333-346.
46. Meyer-Nieberg, S., and Beyer, H.-G. (2007). Self-adaptation in evolutionary algorithms. In F. G. Lobo, C. F. Lima, and Z. Michalewicz (Eds.), Parameter setting in evolutionary algorithms. Studies in computational intelligence, Vol. 54 (pp. 47-75). Berlin: Springer.
47. Ochoa, G., Qu, R., and Burke, E. K. (2009). Analyzing the landscape of a graph based hyper-heuristic for timetabling problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'09), pp. 341-348.
48. Ong, Y.-S., Lim, M.-H., Zhu, N., and Wong, K.-W. (2006). Classification of adaptive memetic algorithms: A comparative study. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36(1):141-152.
49. Osman, I. H., and Laporte, G. (1996). Metaheuristics: A bibliography. Annals of Operations Research, 63(5):513-623.
50. Özcan, E., Bilgin, B., and Korkmaz, E. (2006). Hill climbers and mutational heuristics in hyperheuristics. In Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN 2006). Lecture Notes in Computer Science, Vol. 4193 (pp. 202-211). Berlin: Springer.
51. Özcan, E., Bilgin, B., and Korkmaz, E. (2008). A comprehensive analysis of hyper-heuristics. Intelligent Data Analysis, 12(1):3-23.
52. Pillay, N., and Banzhaf, W. (2009). A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem. European Journal of Operational Research, 197(2):482-491.
53. Poli, R., Woodward, J. R., and Burke, E. K. (2007). A histogram-matching approach to the evolution of bin-packing strategies. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), pp. 3500-3507.
54. Pullan, W. (2008). A population based hybrid metaheuristic for the p-median problem. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2008), pp. 75-82.
55. Qu, R., and Burke, E. K. (2009). Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems. Journal of the Operational Research Society, 60:1273-1285.
56. Qu, R., Burke, E. K., and 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.
57. Rechenberg, I. (1973). Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der boil-ogischen Evolution. Frommann-Holzboog.
58. Reinelt, G. (1991). TSPLIB-A traveling salesman problem library. ORSA Journal on Computing, 3(4):376-384.
59. Ren, Z., Jiang, H., Xuan, J., and Luo, Z. (2010). Ant based hyper heuristics with space reduction: A case study of the p-median problem. In Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). Lecture Notes in Computer Science, Vol. 6238 (pp. 546-555). Berlin: Springer.
60. Resende, M. G. C., and Werneck, R. F. (2003). On the implementation of a swap-based local search procedure for the p-median problem. In Proceedings of the Fifth Workshop on Algorithm Engineering and Experiments, pp. 119-127.
61. Resende, M. G. C., and Werneck, R. F. (2004). A hybrid heuristic for the p-median problem. Journal of Heuristics, 10(1):59-88.
62. Ross, P. (2005). Hyper-heuristics. In E. K. Burke and G. Kendall (Eds.), Search methodologies, (pp. 529-556). Berlin: Springer.
63. Serpell, M. C., and Smith, J. E. (2010). Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms. Evolutionary Computation, 18(3):491-514.
64. Socha, K. (2004). ACO for continuous and mixed-variable optimization. Ant colony, optimization and swarm intelligence. Lecture Notes in Computer Science, Vol. 3172 (pp. 53-61). Berlin: Springer.
65. Socha, K., and Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3):1155-1173.
66. Taillard, É. D., Gambardella, L. M., Gendreau, M., and Potvin, J.-Y. (2001). Adaptive memory programming: A unified view of metaheuristics. European Journal of Operational Research, 135(1):1-16.
67. Teitz, M. B., and Bart, P. (1968). Heuristic methods for estimating the generalized vertex median of a weighted graph. Operations Research, 16(5):955-961.
68. Thierens, D. (2005). An adaptive pursuit strategy for allocating operator probabilities. In Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (GECCO'05), pp. 1539-1546.
69. Vazquez-Rodriguez, J., and Petrovic, S. (2010). A new dispatching rule based genetic algorithm for the multi-objective job shop problem. Journal of Heuristics, 16(6):771-793.
70. Whitaker, R. (1983). A fast algorithm for the greedy interchange for large-scale clustering and median location problems. INFOR, 21(2):95-108.