HyFlex: A benchmark framework for cross-domain heuristic search

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7245 LNCS, Issue , 2012, Pages 136-147

  Ochoa, Gabriela ^a   Hyde, Matthew ^a   Curtois, Tim ^a   Vazquez Rodriguez, Jose A ^a   Walker, James ^a   Gendreau, Michel ^b   Kendall, Graham ^a   McCollum, Barry ^c   Parkes, Andrew J ^a   Petrovic, Sanja ^a   Burke, Edmund K ^d  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

^c QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^d UNIVERSITY OF STIRLING   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIN PACKING; COMBINATORIAL PROBLEM; COMMON SOFTWARE; CROSS-DOMAIN; HEURISTIC SEARCH; MAXIMUM SATISFIABILITY; OPTIMISATIONS; PERMUTATION FLOW SHOPS; PROBLEM DOMAIN; SEARCH OPERATORS; SOFTWARE FRAMEWORKS; TRAVELING SALESMAN;

COMBINATORIAL OPTIMIZATION; COMPUTER PROGRAMMING; HEURISTIC METHODS; MODULAR ROBOTS; VEHICLE ROUTING;

ALGORITHMS;

EID: 84859153492     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29124-1_12     Document Type: Conference Paper

References (22)

1. Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Operations Research/Computer Science Interfaces Series, vol. 45. Springer (2009)
2. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA - A Platform and Programming Language Independent Interface for Search Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494-508. Springer, Heidelberg (2003)
3. Burke, E.K., Curtois, T., Hyde, M., Kendall, G., Ochoa, G., Petrovic, S., Vazquez-Rodriguez, J.A., Gendreau, M.: Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, pp. 3073-3080 (July 2010)
4. Burke, E.K., Gendreau, M., Ochoa, G., Walker, J.D.: Adaptive iterated local search for cross-domain optimisation. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1987-1994. ACM, New York (2011)
5. Burke, E.K., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyperheuristics: An emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457-474. Kluwer (2003)
6. Burke, E.K., Hyde,M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A Classification of Hyper-heuristic Approaches. In: Handbook of Metaheuristics. International Series in Operations Research & Management Science, ch. 15, vol. 146, pp. 449-468. Springer (2010)
7. Chan, C.Y., Xue, F., Ip, W.H., Cheung, C.F.: A hyper-heuristic inspired by pearl hunting. In: International Conference on Learning and Intelligent Optimization (LION 6). LNCS, Springer (to appear, 2012)
8. Cowling, P., Kendall, G., Soubeiga, E.: A Hyperheuristic Approach to Scheduling a Sales Summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176-190. Springer, Heidelberg (2001)
9. Curtois, T.: Staff rostering benchmark data sets. Website (2011), http://www.cs.nott.ac.uk/~tec/NRP/
10. Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter Control in Evolutionary Algorithms. In: Parameter Setting in Evolutionary Algorithms, pp. 19-46. Springer (2007)
11. Fialho, Á., Da Costa, L., Schoenauer, M., Sebag, M.: Extreme Value Based Adaptive Operator Selection. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X 2008. LNCS, vol. 5199, pp. 175-184. Springer, Heidelberg (2008)
12. Di Gaspero, L., Urli, T.: A reinforcement learning approach for the cross-domain heuristic search challenge. In: Proceedings of the 9th Metaheuristics International Conference (MIC 2011), Udine, Italy, July 25-28 (2011)
13. Johnson, D., Demers, A., Ullman, J., Garey, M., Graham, R.: Worst-case performance bounds for simple one-dimensional packaging algorithms. SIAM Journal on Computing 3(4), 299-325 (1974)
14. Misir, M., Verbeeck, K., De Causmaecker, P., Vanden Berghe, G.: An intelligent hyper-heuristic framework for chesc 2011. In: International Conference on Learning and Intelligent Optimization (LION 6). LNCS. Springer (to appear, 2012)
15. Nawaz, M., Enscore Jr., E., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. OMEGA-International Journal of Management Science 11(1), 91-95 (1983)
16. Ochoa, G., Hyde, M.: The Cross-domain Heuristic Search Challenge (CHeSC 2011). Website (2011), http://www.asap.cs.nott.ac.uk/chesc2011/
17. Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Transactions on Systems, Man, and Cybernetics, Part B 36(1), 141-152 (2006)
18. Ozcan, E., Kheiri, A.: A hyper-heuristic based on random gradient, greedy and dominance. In: Proceedings of the 26th International Symposium on Computer and Information Sciences ISCIS 2011, London, UK, July 25-28 (2011)
19. Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Computers and Operations Research 34, 2403-2435 (2007)
20. Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, ch.17, pp. 529-556. Springer (2005)
21. Smith, J.E.: Co-evolving memetic algorithms: A review and progress report. IEEE Transactions in Systems, Man and Cybernetics, part B 37(1), 6-17 (2007)
22. Walker, J.D., Ochoa, G., Gendreau, M., Burke, E.K.: Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework. In: International Conference on Learning and Intelligent Optimization (LION 6). LNCS. Springer (to appear, 2012)