This presentation provides a review of the early work of hyper-heuristics, current work that is being undertaken followed by a discussion of open research challenges. This is a Powerpoint Slideshow version. A PDF version is also available.
4. Fisher H. and Thompson G.L. (1963) Probabilistic Learning Combinations of Local Job-shop Scheduling Rules. In Muth J.F. and Thompson G.L. (eds) Industrial Scheduling, Prentice Hall Inc., New Jersey, 225-251 Based on (I assume) Fisher H. and Thompson G.L. (1961) Probabilistic Learning Combinations of Local Job-shop Scheduling Rules. In Factory Scheduling Conference, Carnegie Institute of Technology
22. The chunks abc means to put the first untackled task of the ath uncompleted job into the earliest place it will fit in the developing schedule, then put the bth uncompleted job into ….
28. Used in the context of an automated theorem prover
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30. Remarks Pi = (Ai x Ti) + (Bi x Si) where Pi the priority index for job i at its current stage Ai a 1 x m coefficient vector for job i Ti a m x 1 vector which contains the remaining operation times for job i in process order Bi the due date priority coefficient for job i Sithe due date slack for job i m the maximum number of processing stages for jobs 1 to i
31. Remarks Pi = (Ai x Ti) + (Bi x Si) where Pi the priority index for job i at its current stage Ai a 1 x m coefficient vector for job i Ti a m x 1 vector which contains the remaining operation times for job i in process order Bi the due date priority coefficient for job i Sithe due date slack for job i m the maximum number of processing stages for jobs 1 to i A = (1,0,0,0,0,…,0), B = 0 Shortest Imminent Operation Time A = (0,0,0,0,0,…,0), B = 1 Due Date Sequencing
32. Remarks Pi = (Ai x Ti) + (Bi x Si) where Pi the priority index for job i at its current stage Ai a 1 x m coefficient vector for job i Ti a m x 1 vector which contains the remaining operation times for job i in process order Bi the due date priority coefficient for job I Sithe due date slack for job i m the maximum number of processing stages for jobs 1 to i A search is performed over Ai and Bi in order to cause changes in the processing sequences.
33. Norenkov I. P. and Goodman E D. (1997) Solving Scheduling Problems via Evolutionary Methods for Rule Sequence Optimization. In proceedings of the 2nd World Conference on Soft Computing (WSC2)
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35. The allele at the ith position is the heuristic to be applied at the ith step of the scheduling process.
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37. Storer R.H., Wu S.D. and Vaccari R. (1992) New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509
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39. H2 Hn Heuristics to Choose Heuristics Hyper-heuristic Data flow Domain Barrier Data flow Set of low level heuristics H1 …… Evaluation Function
48. Adapt the ordering by a heuristic modifier according to the penalty imposed by certain features
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50. Heuristics could be one off (disposal) heuristics or could be applicable to many problem instancesData flow Domain Barrier Data flow Set of low level heuristics H1 …… Evaluation Function
51. Generating heuristics Burke E. K., Hyde M. and Kendall G. Evolving Bin Packing Heuristics With Genetic Programming. In Proceedings of the 9th International Conference on Problem Parallel Solving from Nature (PPSN 2006), pp 860-869, LNCS 4193, Reykjavik, Iceland, 9-13 Sepetmber 2006
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53. First-fit heuristic evolved from Genetic Programming without human input on benchmark instancesFor each piece, p, not yet packed For each bin, i output = evaluate(p, fullness of i, capacity of i) if (output > 0) place piece p in bin i break fi End For End For
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58. Why are some hyper-heuristics better than others – and on what class of problems?