Hyper-heuristics: Past Present and Future
The University of Nottingham




                                               ...
Contents
The University of Nottingham




                               Past
                               • A selection...
Contents
The University of Nottingham




                               Past
                               • A selection...
The University of Nottingham




                               Fisher H. and Thompson G.L. (1963) Probabilistic Learning
...
Good
                                                                 Facility Order Matrix
The University of Nottingham

...
Good
                                                      Facility Order Matrix
The University of Nottingham



         ...
Good
                                                       Facility Order Matrix
The University of Nottingham



        ...
• Monte Carlo: 58 time Units
The University of Nottingham



                               • SIO:         67 time units
 ...
Remarks
The University of Nottingham




                               • Not sure about reproducibility (e.g.
           ...
The University of Nottingham




                               Fang H-L., Ross P. and Corne D. (1993) A Promising genetic...
Representation
The University of Nottingham




                               • For a j x m problem, a string represents ...
Other Remarks
The University of Nottingham




                               • Considered Job-Shop Scheduling and Open-
 ...
The University of Nottingham




                               Denzinger J. and Fuchs M. (1997) High Performance ATP Syst...
Remarks
The University of Nottingham




                               • The first paper to use the term Hyper-heuristic
...
The University of Nottingham




                               O’Grady P.J. and Harrison (1985) A General Search Sequenci...
Remarks
The University of Nottingham




                               Pi = (Ai x Ti) + (Bi x Si)

                      ...
Remarks
                               Pi = (Ai x Ti) + (Bi x Si)
The University of Nottingham




                       ...
Remarks
                               Pi = (Ai x Ti) + (Bi x Si)
The University of Nottingham




                       ...
The University of Nottingham




                               Norenkov I. P. and Goodman E D. (1997) Solving Scheduling
...
Remarks
The University of Nottingham




                               • Similar in idea to Fang, Ross and Corne (1994)

...
Other (Selected) Papers
The University of Nottingham




                               • Crowston W.B., Glover F., Thomps...
Contents
The University of Nottingham




                               Past
                               • A selection...
Heuristics to Choose Heuristics
The University of Nottingham




                                          Hyper-heuristic...
Choice Function
The University of Nottingham



                               • f1 + f2 + f3
                            ...
Tabu Search
The University of Nottingham




                               • Low level heuristics compete
               ...
Case Based Heuristic Selection
The University of Nottingham




                               • Find heuristics that work...
Adaptive Ordering Strategies
The University of Nottingham




                               • Based on Squeaky Wheel
    ...
Contents
The University of Nottingham




                               Past
                               • A selection...
Generating heuristics
The University of Nottingham



                                                                    ...
Generating heuristics
The University of Nottingham




                               Burke E. K., Hyde M. and Kendall G. ...
Generating heuristics
The University of Nottingham




                               • Evolves a control program that
   ...
Contents
The University of Nottingham




                               Past
                               • A selection...
Results on Standard Datasets
The University of Nottingham




                               •Many early papers investigat...
Benchmark datasets
The University of Nottingham




                               •We need to add to resources such as
  ...
Comparison against benchmarks
The University of Nottingham




                               •Using the “good enough, soo...
Ant Algorithm based Hyper-heuristics
The University of Nottingham




                               •Ant algorithms draw ...
Ant Algorithm based hyper-heuristics
The University of Nottingham




                                     Trail          ...
Ant Algorithm based hyper-heuristics
The University of Nottingham




                                  Heuristic         ...
“Good enough, soon enough, cheap
                                           enough”
The University of Nottingham




     ...
“Good enough, soon enough, cheap
                                           enough”
The University of Nottingham




     ...
“Good enough, soon enough, cheap
                                           enough”
The University of Nottingham




     ...
“Good enough, soon enough, cheap
                                           enough”
The University of Nottingham




     ...
Comparing Hyper-heuristics
The University of Nottingham




                               •How can we compare different h...
Anti-heuristics
The University of Nottingham




                               •There is/has been a significant amount
  ...
Minimal Heuristics
The University of Nottingham




                               •Many of the hyper-heuristic papers
   ...
Evolve heuristics
The University of Nottingham




                               •We can ignore “choose a set of low leve...
Co-evolution
The University of Nottingham




                               •Heuristics compete for survival
            ...
Hybridisations
The University of Nottingham




                               •Is there anything to be gained from
      ...
User interaction
The University of Nottingham




                               •How can users interact with hyper-
     ...
Framework
The University of Nottingham




                               •There is a large learning curve and high
      ...
A unifying theory
The University of Nottingham




                               •What is the formal relationship between...
A unifying theory
The University of Nottingham




                               •Can we analyse the landscape of the
   ...
A unifying theory
The University of Nottingham




                               •Can we offer convergence guarantees?
  ...
Questions/Discussion
The University of Nottingham




                                          Graham Kendall, Hyper-heur...
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Hyperheuritics: Past, Present and Future

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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 PDF Slideshow. A Powerpoint Slideshow version is also available.

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Hyperheuritics: Past, Present and Future

  1. 1. Hyper-heuristics: Past Present and Future The University of Nottingham Graham Kendall gxk@cs.nott.ac.uk Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  2. 2. Contents The University of Nottingham Past • A selection of early work Present • Current State of the Art Albert Einstein Future 1879 - 1955 • Potential Research Directions for the Future “We can't solve problems by using the same kind of thinking we used when we created them.” Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  3. 3. Contents The University of Nottingham Past • A selection of early work Present • Current State of the Art Albert Einstein Future 1879 - 1955 • Potential Research Directions for the Future “We can't solve problems by using the same kind of thinking we used when we created them.” Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  4. 4. The University of Nottingham 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 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  5. 5. Good Facility Order Matrix The University of Nottingham Number 1 3(1) 1(3) 2(6) 4(7) 6(3) 5(6) 2 2(8) 3(5) 5(10) 6(10) 1(10) 4(4) 3 3(5) 4(4) 6(8) 1(9) 2(1) 5(7) 4 2(5) 1(5) 3(5) 4(3) 5(8) 6(9) 5 3(9) 2(3) 5(5) 6(4) 1(3) 4(1) 6 2(3) 4(3) 6(9) 1(10) 5(4) 3(1) 6 x 6*6 Test Problem (times in brackets) “The number of feasible active schedules is, by a conservative estimate, well over a million, so their complete enumeration is out of the question.” • Also 10 (jobs) x 10 (operations) and 20 (jobs) x 5 (operations) problems Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  6. 6. Good Facility Order Matrix The University of Nottingham Number 1 3(1) 1(3) 2(6) 4(7) 6(3) 5(6) 2 2(8) 3(5) 5(10) 6(10) 1(10) 4(4) 3 3(5) 4(4) 6(8) 1(9) 2(1) 5(7) 4 2(5) 1(5) 3(5) 4(3) 5(8) 6(9) 5 3(9) 2(3) 5(5) 6(4) 1(3) 4(1) 6 2(3) 4(3) 6(9) 1(10) 5(4) 3(1) 6 x 6*6 Test Problem (times in brackets) Job 3, 1, 2, 5, 4, 6 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  7. 7. Good Facility Order Matrix The University of Nottingham Number 1 3(1) 1(3) 2(6) 4(7) 6(3) 5(6) 2 2(8) 3(5) 5(10) 6(10) 1(10) 4(4) 3 3(5) 4(4) 6(8) 1(9) 2(1) 5(7) 4 2(5) 1(5) 3(5) 4(3) 5(8) 6(9) 5 3(9) 2(3) 5(5) 6(4) 1(3) 4(1) 6 2(3) 4(3) 6(9) 1(10) 5(4) 3(1) 6 x 6*6 Test Problem (times in brackets) • Two Rules • SIO: Shortest Imminent Operation (“First on, First Off”) • LRT: Longest Remaining Time • Only require knowledge of “your” machine Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  8. 8. • Monte Carlo: 58 time Units The University of Nottingham • SIO: 67 time units • LRT: 61 time units • Optimal: 55 time units • SIO should be used initially (get the machines to start work) and LRT later (work on the longest jobs) • Why not combine the two heuristics? • Four learning models, rewarding good heuristic selection Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  9. 9. Remarks The University of Nottingham • Not sure about reproducibility (e.g. reward/punishment functions) • An unbiased random combination of scheduling rules is better than any of them taken separately • “Learning is possible, but there is a question as to whether learning is desirable given the effectiveness of the random combination” • “It is not clear what is being learnt as the original conjecture was not strongly supported” • “It is likely that combinations of 5-10 rules would out-perform humans” Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  10. 10. The University of Nottingham Fang H-L., Ross P. and Corne D. (1993) A Promising genetic Algorithm Approach to Job-Shop Scheduling, Reschecduling, and Open-Shop Scheduling Problems. In Forrest S. (ed) Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, 375-383 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  11. 11. Representation The University of Nottingham • For a j x m problem, a string represents j x m chunks. • The chunk is atomic from a GA perspective. • 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 …. • A schedule builder decodes the chromosome. • Fairly standard GA e.g. population size of 500, rank based selection, elitism, 300 generations, crossover rate 0.6, adaptive mutation rate Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  12. 12. Other Remarks The University of Nottingham • Considered Job-Shop Scheduling and Open- Shop Scheduling • Experimented with different GA parameters • Results compared favourably with best known or optimal Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  13. 13. The University of Nottingham Denzinger J. and Fuchs M. (1997) High Performance ATP Systems by Combining Several AI Methods. In proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97), 102-107 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  14. 14. Remarks The University of Nottingham • The first paper to use the term Hyper-heuristic • Used in the context of an automated theorem prover • A hyper-heuristic stores all the information necessary to reproduce a certain part of the proof and is used instead of a single heuristic Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  15. 15. The University of Nottingham O’Grady P.J. and Harrison (1985) A General Search Sequencing Rule for Job Shop Sequencing. International Journal of Production Research, 23(5), 961-973 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  16. 16. Remarks The University of Nottingham 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 Si the due date slack for job i m the maximum number of processing stages for jobs 1 to i Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  17. 17. Remarks Pi = (Ai x Ti) + (Bi x Si) The University of Nottingham 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 Si the 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 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  18. 18. Remarks Pi = (Ai x Ti) + (Bi x Si) The University of Nottingham 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 Si the 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. Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  19. 19. The University of Nottingham 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) Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  20. 20. Remarks The University of Nottingham • Similar in idea to Fang, Ross and Corne (1994) • The allele at the ith position is the heuristic to be applied at the ith step of the scheduling process. • Comparison with using eight single heuristics and the Heuristic Combination Method (HCM) was found to be superior. Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  21. 21. Other (Selected) Papers The University of Nottingham • Crowston W.B., Glover F., Thompson G.L. and Trawick J.D. (1963) Probabilistic and Parameter Learning Combinations of Local Job Shop Scheduling Rules. ONR Research Memorandum, GSIA, Carnegie Mellon University • 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 • Battiti R. (1996) Reactive Search: Toward Self Tuning Heuristics. In Rayward-Smith R.J., Osman I.H., Reeves C.R. and Smith G.D. (eds) Modern Heuristics Search methods, John Wiley, 61-83 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  22. 22. Contents The University of Nottingham Past • A selection of early work Present (Heuristics to Choose Heuristics) • Current State of the Art Albert Einstein Future 1879 - 1955 • Potential Research Directions for the Future “We can't solve problems by using the same kind of thinking we used when we created them.” Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  23. 23. Heuristics to Choose Heuristics The University of Nottingham Hyper-heuristic Data flow Domain Barrier Data flow Set of low level heuristics H1 H2 Hn …… Evaluation Function Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  24. 24. Choice Function The University of Nottingham • f1 + f2 + f3 • f1 = How well has each heuristic performed • f2 = How well have pairs of heuristics performed • f3 = Time since last called Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  25. 25. Tabu Search The University of Nottingham • Low level heuristics compete with each other • Recent heuristics are made tabu • Rank low level heuristics based on their estimated performance potential Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  26. 26. Case Based Heuristic Selection The University of Nottingham • Find heuristics that worked well in previous similar problem solving situations • Features discovered in similarity measure – key research issue Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  27. 27. Adaptive Ordering Strategies The University of Nottingham • Based on Squeaky Wheel Optimisation • Consider constructive heuristics as orderings • Adapt the ordering by a heuristic modifier according to the penalty imposed by certain features • Generative Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  28. 28. Contents The University of Nottingham Past • A selection of early work Present (Generating Heuristics) • Current State of the Art Future • Potential Research Directions for the Future Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  29. 29. Generating heuristics The University of Nottingham Hyper-heuristic • Rather than supply a set of low level heuristics, generate the Data flow heuristics automatically Domain Barrier • Heuristics could be one off Data flow (disposal) heuristics or could be applicable to many problem instances Set of low level heuristics H1 H2 Hn …… Evaluation Function Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  30. 30. Generating heuristics The University of Nottingham 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 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  31. 31. Generating heuristics The University of Nottingham • Evolves a control program that decides whether to put a given piece into a given bin • First-fit heuristic evolved from Genetic Programming without human input on benchmark instances For 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 Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  32. 32. Contents The University of Nottingham Past • A selection of early work Present • Current State of the Art Albert Einstein Future 1879 - 1955 • Potential Research Directions for the Future “We can't solve problems by using the same kind of thinking we used when we created them.” Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  33. 33. Results on Standard Datasets The University of Nottingham •Many early papers investigated JSSP. There is an opportunity to investigate if the current state of the art is able to beat these and set new benchmarks •Why not apply hyper-heuristics to more current benchmarks (TSP, VRP, QAP etc.). Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  34. 34. Benchmark datasets The University of Nottingham •We need to add to resources such as OR-LIB so that we are able to compare hyper-heuristic approaches. •We need to have access to benchmarks that are understandable, perceived as fair and which are not open to many interpretations. Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  35. 35. Comparison against benchmarks The University of Nottingham •Using the “good enough, soon enough, cheap enough” mantra we don’t claim to be competitive with bespoke solutions, but we are interested if we can beat best known solutions. •Why are some hyper-heuristics better than others – and on what class of problems? •Robustness vs quality and how do we measure that? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  36. 36. Ant Algorithm based Hyper-heuristics The University of Nottingham •Ant algorithms draw their inspiration from the way ants forage for food. •Two major elements to an ant algorithm. •Pheromone values •Heuristic values Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  37. 37. Ant Algorithm based hyper-heuristics The University of Nottingham Trail Visibility Intensity Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  38. 38. Ant Algorithm based hyper-heuristics The University of Nottingham Heuristic Visibility Synergy Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  39. 39. “Good enough, soon enough, cheap enough” The University of Nottingham •What does this actually mean? •Will the scientific community accept that this is a fair way to compare results? Different Evaluations Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  40. 40. “Good enough, soon enough, cheap enough” The University of Nottingham •How do we know if a solution is “good enough”? •User feedback? •Within a given value of best known solution? •We get bored running the Not Good Enough! algorithm? •The cost of accepting the solution is acceptable? •Two evaluation mechanisms? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  41. 41. “Good enough, soon enough, cheap enough” The University of Nottingham •How do we know if a solution is “soon enough”? •Meet a critical deadline? •Run as long as we can? •Can be embedded in a realtime system? Soon Enough! Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  42. 42. “Good enough, soon enough, cheap enough” The University of Nottingham •How do we know if a solution is “cheap enough”? •Can be embedded in “off-the-shelf” software? •Development costs are significantly lower writing a bespoke system? Cheap Enough! •Can be run on a standard PC, rather than requiring specialised hardware? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  43. 43. Comparing Hyper-heuristics The University of Nottingham •How can we compare different hyper- heuristics so that reviewers have a way of fairly judging new contributions •What do we mean by “One hyper- heuristic is better than another”? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  44. 44. Anti-heuristics The University of Nottingham •There is/has been a significant amount of research investigating how we can “choose which heuristic to select at each decision point” •There could also be some benefit in investigating hyper-heuristics that are obviously bad and seeing if the hyper- heuristic is able to learn/adapt not to use them Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  45. 45. Minimal Heuristics The University of Nottingham •Many of the hyper-heuristic papers effectively say “choose a set of low level heuristics…” •But, can we define a minimal set of heuristics that operate well across different problems (e.g. add, delete and swap)? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  46. 46. Evolve heuristics The University of Nottingham •We can ignore “choose a set of low level heuristics…” if we can generate our own set of human competitive heuristics •We have utilised genetic programming and adaptive constructive heuristics but there remains lots of scope for further investigation. Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  47. 47. Co-evolution The University of Nottingham •Heuristics compete for survival •Similarities with genetic algorithms etc., but there is a wide scope of possible research in this area. Arthur Samuel 1901 – 1990 An AI Pioneer Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  48. 48. Hybridisations The University of Nottingham •Is there anything to be gained from hybridising various methodologies? •There has been success with exact methods and meta-heuristics •What about hybridising hyper-heuristics with meta-heuristics, exact approaches, user interaction etc? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  49. 49. User interaction The University of Nottingham •How can users interact with hyper- heuristics? •Introduce/delete heuristics as the search progresses? •Prohibit some areas of the search space? •Provide a time/quality trade off? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  50. 50. Framework The University of Nottingham •There is a large learning curve and high buy-in to develop a hyper-heuristic •Tools such as GA-LIB help the community to utilise the tools and to carry out research •But, what should this framework enable you to do? Choose heuristics, generate heuristics? Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  51. 51. A unifying theory The University of Nottingham •What is the formal relationship between heuristics, meta-heuristics and hyper- heuristics (and even exact methods)? Stephen Hawking 1942 - Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  52. 52. A unifying theory The University of Nottingham •Can we analyse the landscape of the different search methodologies? •Can we move between different search Stephen Hawking spaces during the search? 1942 - Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  53. 53. A unifying theory The University of Nottingham •Can we offer convergence guarantees? •Can we offer guarantees of solution quality and/or robustness? Stephen Hawking 1942 - Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)
  54. 54. Questions/Discussion The University of Nottingham Graham Kendall, Hyper-heuristics: Past, Present and Future (uploaded to Slideshare.com : 25th April 2010)

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