Selection hyper-heuristics in dynamic environments

Journal of the Operational Research Society

Volumn 64, Issue 12, 2013, Pages 1753-1769

  Kiraz, B ^a   Etaner Uyar, A S ^a   Ozcan E ^b  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

^b UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Decision support; Dynamic environments; Heuristics; Hyper heuristics; Meta heuristics; Moving peaks benchmark

Indexed keywords

DECISION SUPPORT SYSTEMS; OPTIMIZATION;

DECISION SUPPORTS; DYNAMIC ENVIRONMENTS; HEURISTICS; HYPER-HEURISTICS; META HEURISTICS; MOVING PEAKS BENCHMARKS;

HEURISTIC METHODS;

EID: 84888058430     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/jors.2013.24     Document Type: Article

References (47)

1. Ayob M and Kendall G (2003). A Monte Carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In: Proceedings of the International Conference on Intelligent Technologies, pp 132-141
2. Bai R, Blazewicz J, Burke EK, Kendall G and McCollum B (2007). A simulated annealing hyper-heuristic methodology for flexible decision support. Technical Report NOTTCS-TR-2007-8, School of CSIT, University of Nottingham
3. Bai R and Kendall G (2005). An investigation of automated planograms using a simulated annealing based hyper-heuristics. In: Ibaraki T, Nonobe K and Yagiura M (eds). Metaheuristics: Progress as Real Problem Solver-(Operations Research/ Computer Science Interface Series, Vol. 32). Springer:New York, pp 87-108
4. Beyer HG and Schwefel HP (2002). Evolution strategies-A comprehensive introduction. Natural Computing 1(1): 3-52
5. Bilgin B, Özcan E and Korkmaz EE (2006). An experimental study on hyper-heuristics and exam timetabling. In: Burke EK and Rudova H (eds). Proceedings of the 6th International Conference on Practice and Theory of Automated Timetabling. Springer: New York, pp 123-140
6. Branke J (1999). Memory enhanced evolutionary algorithms for changing optimization problems. In: Angeline PJ,Michalewicz Z, Schoenauer M, Yao X and Zalzala A (eds). Congress on Evolutionary Computation CEC 99, Vol. 3. IEEE: New York, pp 1875-1882
7. Branke J (2002). Evolutionary Optimization in Dynamic Environments. Kluwer: Dordrecht, M.A.
8. Branke J, Salihoǧlu E and Uyar AŞ (2005). Towards an analysis of dynamic environments. In: Beyer H-G et al (eds). Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO'05. ACM: New York, pp 1433-1440
9. Burke E, Hart E, Kendall G, Newall J, Ross P and Schulenburg S (2003). Hyper-heuristics: An emerging direction in modern search technology. In: Glover F and Kochenberger G (eds). Handbook of Metaheuristics. Kluwer: Dordrecht, MA, pp 457-474
10. Burke EK, Hyde M, Kendall G, Ochoa G, Özcan E and Rong Q (2009a). A survey of hyper-heuristics. Technical report. Burke EK, Hyde MR, Kendall G, Ochoa G, Özcan E and
11. Woodward JR (2009b). Exploring hyper-heuristic methodologies with genetic programming. In: Kacprzyk J, Jain LC, Mumford CL and Jain LC (eds). Computational Intelligence. Vol. 1. Intelligent Systems Reference Library. Springer: New York, pp 177-201
12. Burke EK, Hyde M, Kendall G, Ochoa G,Özcan E and Woodward JR (2010a). A classification of hyper-heuristic approaches. In: Gendreau M and Potvin J-Y (eds). Handbook of Metaheuristics. Vol. 146. International Series in Operations Research and Management Science. Springer: New York, pp 449-468
13. Burke EK, Kendall G,MisirM and Özcan E (2010b).Monte Carlo hyper-heuristics for examination timetabling. Annals of Operations Research 196(1): 1-18
14. Chakhlevitch K and Cowling P (2008). Hyperheuristics: Recent developments. In: Cotta C, Sevaux M and Sirensen K (eds). Adaptive andMultilevelMetaheuristics. Vol. 136. Studies inComputational Intelligence. Springer: Berlin, Heidelberg, pp 3-29
15. Cobb HG (1990). An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, timedependent nonstationary environments. Technical Report AIC-90-001, Naval Research Lab, Washington D.C.
16. Cowling P, Kendall G and Soubeiga E (2000). A hyper-heuristic approach to scheduling a sales summit. In: Burke EK and Erben W (eds). Practice and Theory of Automated Timetabling III: Third International Conference, PATAT 2000, Vol. 2079.LNCS, Springer: New York
17. Cowling P, Kendall G and Soubeiga E (2001). A parameter-free hyper-heuristic for scheduling a sales summit. In: Proceedings of the 4th Metaheuristic International Conference, pp 127-131
18. Cowling P, Kendall G and Soubeiga E (2002). Hyperheuristics: A tool for rapid prototyping in scheduling and optimisation. In: Cagnoni S, Gottlieb J, Hart E, Middendorf M and Raidl GR (eds). EvoWorkShops. Vol. 4193. Lecture Notes in Computer Science. Springer: New York, pp 1-10
19. Crowston WB, Glover F, Thompson GL and Trawick JD (1963). Probabilistic and parametric learning combinations of local job shop scheduling rules. ONR Research Memorandum, GSIA, Carnegie Mellon University, Pittsburgh (117
20. Cruz C, Gonzalez J and Pelta D (2011). Optimization in dynamic environments: A survey on problems, methods and measures. Soft Computing-A Fusion of Foundations, Methodologies and Applications 15(7): 1427-1448
21. Denzinger J, FuchsMand FuchsM(1997). High performance ATP systems by combining several AI methods. In: 4th Asia-Pacific Conference on SEAL. Morgan Kaufmann: Los Altos, CA, pp 102-107
22. Dowsland KA, Soubeiga E and Burke EK (2007). A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation. European Journal of Operational Research 179(3): 759-774
23. Fisher H and Thompson GL (1963). Probabilistic learning combinations of local job-shop scheduling rules. In: Muth JF and Thompson GL (eds). Industrial Scheduling. Prentice-Hall: Englewood Cliffs, NJ, pp 225-251
24. Gibbs J, Kendall G and Özcan E (2011). Scheduling english football fixtures over the holiday period using hyper-heuristics. In: Schaefer R, Cotta C, Kolodziej J and Rudolph G (eds). Parallel Problem Solving from Nature-PPSN XI. Vol. 6238. Lecture Notes in Computer Science. Springer: Berlin/Heidelberg, pp 496-505
25. Grefenstette JJ (1992). Genetic algorithms for changing environments. In: Maenner R and Manderick B (eds). Proceedings of Parallel Problem Solving from Nature, pp 137-1446
26. Hansen N (2011). The CMA evolution strategy: A tutorial. Technical report, 28 June
27. Hansen N and Ostermeier A (2001). Completely derandomized selfadaptation in evolution strategies. Evolutionary Computation 9(2): 159-195
28. Hansen N, Mudie;ller SD and Koumoutsakos P (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation 11(1): 1-18
29. Jin Y and Branke J (2005). Evolutionary optimization in uncertain environments-A survey. IEEE Transactions on Evolutionary Comp 9(3): 303-317
30. Kendall G and Mohamad M (2004). Channel assignment in cellular communication using a great deluge hyperheuristic. In: Mastorakis N, Mladenov V and Savkovic-Stevanovic J (eds). IEEE International Conference on Network, World Scientific and Engineering Academy and Society (WSEAS): Stevens Point, WI, pp 769-773
31. Kiraz B and Topcuoglu HR (2010). Hyper-heuristic approaches for the dynamic generalized assignment problem. In: 10th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE: New York, pp 1487-1492
32. Kiraz B, Uyar A.Ş andÖzcan E (2011). An investigation of selection hyper-heuristics in dynamic environments. In: Di Chio C, et al (eds). Proceedings of EvoApplications 2011, Vol. 6624 of LNCS, Springer: New York
33. Lewis J, Hart E and Ritchie G (1998). A comparison of dominance mechanisms and simple mutation on nonstationary problems. In: Eiben AE, Bäck T, Schoenauer M and Schwefel HP (eds). Proceedings of Parallel Problem Solving from Nature. Springer: New York, pp 139-148
34. Lundy M and Mees A (1986). Convergence of an annealing algorithm. Mathematical Programming 34(1): 111-124
35. Morrison RW (2004). Designing Evolutionary Algorithms for Dynamic Environments. Springer: New York
36. Nareyek A (2001). Choosing search heuristics by non-stationary reinforcement learning. In: Resende MGC and de Sous JP (eds). Metaheuristics: Computer Decision-Making. Kluwer Academic Publishers: Norwell, MA, pp 523-544
37. Özcan E, Bilgin B and Korkmaz EE (2008). A comprehensive analysis of hyper-heuristics. Intelligent Data Analysis 12(1): 3-23
38. Özcan E, Uyar AŞ and Burke E (2009). A greedy hyper-heuristic in dynamic environments. In: Rothlauf F (ed). GECCO 2009 Workshop on Automated Heuristic Design: Crossing the Chasm for Search Methods. ACM: New York, pp 2201-2204
39. Özcan E, MisirM, Ochoa G and Burke EK (2010). A reinforcement learning-Great-deluge hyper-heuristic for examination timetabling. International Journal of AppliedMetaheuristic Computing 1(1): 39-59
40. Ross P (2005). Hyper-heuristics. In: Burke EK and Kendall G (eds). Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Chapter 17. Springer: New York, pp 529-556
41. Salihoǧlu E (2005). Towards an analysis of dynamic environments. Master's thesis, Istanbul Technical University, http://web.itu .edu.tr/etaner/thesis-salihoglu-05.pdf
42. Ursem RK (2000). Multinational GA optimization techniques in dynamic environments. In: Whitley D, Goldberg D, Cantu-Paz E, Spector L, Parmee I and Beyer H-G (eds). Proceedings of the Genetic and Evolutionary Conference. Morgan Kaufmann: Burlington, MA, pp 19-26
43. Uyar AŞ and Harmanci AE (2005). A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments. Soft Computing 9(11): 803-814
44. Vavak F, Jukes K and Fogarty TC (1997). Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search. In: Badie;ck T (ed). Proceedings of the International Conference on Genetic Algorithms. Morgan Kaufmann: Los Altos, CA, pp 719-726
45. Yang S (2008). Genetic algorithms with memory-And elitism-based immigrants in dynamic environments. Evolutionary Computation 16(3): 385-416
46. Yang S, and Yao X (2008). Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Comp 12(5): 542-561
47. Yang S, Ong YS and Jin Y (eds) (2007). Evolutionary Computation in Dynamic and Uncertain Environments. Vol. 51. Studies in Computational Intelligence. Springer: New York