Abstract
This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.
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References
Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Operations Research/Computer Science Interfaces Series, vol. 45. Springer (2009)
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)
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)
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)
Burke, E.K., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer (2003)
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)
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)
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)
Curtois, T.: Staff rostering benchmark data sets. Website (2011), http://www.cs.nott.ac.uk/~tec/NRP/
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)
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)
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)
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)
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)
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)
Ochoa, G., Hyde, M.: The Cross-domain Heuristic Search Challenge (CHeSC 2011). Website (2011), http://www.asap.cs.nott.ac.uk/chesc2011/
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)
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)
Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Computers and Operations Research 34, 2403–2435 (2007)
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)
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)
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)
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Ochoa, G. et al. (2012). HyFlex: A Benchmark Framework for Cross-Domain Heuristic Search. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_12
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