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Cluster Computing

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21.08.2017

Deja Vu: a hyper heuristic framework with Record and Recall (2R) modules

Erschienen in: Cluster Computing | Sonderheft 3/2019

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Abstract

Despite the success of heuristic methods in solving real-world problems, there are still some difficulties in terms of easily applying them to newly encountered problems, or even new instances of similar problems. In addition, the little or no understanding of why different heuristics work effectively (or not) in certain situations does not facilitate simple choices of which approach to use in which situation. This paper proposes a new hyper heuristic framework named Deja Vu to address these issues. As the names suggests, it retrieves the stored solution of already solved problems for the new but similar problems. This makes the our system efficient and knowledge rich. The performance of Deja Vu is tested on the data sets with varying difficulty. Deja Vu has shown promising results on almost all the occasions.

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Boskovitz, V., Guterman, H.: An adaptive neuro-fuzzy system for automatic image segmentation and edge detection. IEEE Trans. on Fuzzy Syst. 10(2), 247–262 (2002)CrossRef

Burke, E., Curtois, T., Hyde, M., Ochoa, G., Vázquez Rodríguez, J.A.: Hyflex: a benchmark framework for cross-domain heuristic search. CoRR, abs/1107.5462 (2011)

Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)CrossRef

Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: International Conference on the Practice and Theory of Automated Timetabling, Springer, pp. 176–190 (2000)

Crowston, W.B., Glover, F., Trawick, J.D.: Probabilistic and Parametric Learning Combinations of Local Job Shop Scheduling Rules. Carnegie Institute of Technology, Graduate School of Industrial Administration, Pittsbuggh, PA (1963)

de Sá, A.G.C., Pinto, W.J.G.C., Oliveira, L.O.V.B., Pappa, G.L.: RECIPE: a grammar-based framework for automatically evolving classification pipelines. Springer, Cham (2017)

Elyasaf, A., Vaks, P., Milo, N., Sipper, M., Ziv-Ukelson, M.: Learning heuristics for mining RNA sequence-structure motifs, pp. 21–38. Springer, Cham (2016)

Fisher, H., Thompson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Industrial Scheduling, pp. 225–251. Prentice-Hall (1963)

Hart, E., Ross, P.: Solving a real-world problem using an evolving heuristically driven schedule builder. Evol. Comput. 6(1), 61–80 (1998)CrossRef

10.

Ho, T.K., Baird, H.S.: Pattern classification with compact distribution maps. Comput. Vis. Image Underst. 70(1), 101–110 (1998)CrossRef

11.

Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 289–300 (2002)CrossRef

12.

Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scaling. In: European Conference on Genetic Programming, pp. 70–82. Springer, Berlin (2003)

13.

Kheiri, A., Özcan, E.: An iterated multi-stage selection hyper-heuristic. Eur. J. Oper. Res. 250(1), 77–90 (2016)MathSciNetCrossRef

14.

Maashi, M., Kendall, G., Özcan, E.: Choice function based hyper-heuristics for multi-objective optimization. Appl. Soft Comput. 28, 312–326 (2015)CrossRef

15.

Maashi, M., Özcan, E., Kendall, G.: A multi-objective hyper-heuristic based on choice function. Expert Syst. Appl. 41(9), 4475–4493 (2014)CrossRef

16.

Macià Antolínez, N,. et al. Data complexity in supervised learning: A far-reaching implication. (2011)

17.

Mariani, T., Guizzo, G., Vergilio, S.R., Pozo, A.T.R.: Grammatical evolution for the multi-objective integration and test order problem. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO ’16, pp. 1069–1076, New York, NY, USA, 2016. ACM

18.

Mendes, A., Togelius, J., Nealen, A.: Hyper-heuristic general video game playing. In: Computational Intelligence and Games (CIG), 2016 IEEE Conference on, pp. 1–8. IEEE (2016)

19.

Mladenović, N., Hansen, P.: Variable neighborhood search. Comput Oper Res 24(11), 1097–1100 (1997)MathSciNetCrossRef

20.

Montazeri, M., Baghshah, M.S., Enhesari, A.: Hyper-heuristic algorithm for finding efficient features in diagnose of lung cancer disease. arXiv preprint arXiv:1512.04652 (2015)

21.

Orriols-Puig, A., Macia, N., Ho, T.K.: Documentation for the data complexity library in c++, p. 196. Universitat Ramon Llull La Salle, Barcelona (2016)

22.

Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Class. 10, 61–74 (1999)

23.

Rankhambe, J., Pandharpatte, R.M.: A survey on examination scheduling problem (esp) and hyper-heuristics approaches for solving esp. In: Information Processing (ICIP), 2015 International Conference on, pp. 254–259. IEEE, (2015)

24.

Ryser-Welch, P., Miller, J.E.: A review of hyper-heuristic frameworks. In: Proceedings of the Evo20 Workshop, AISB (2014)

25.

Samulowitz, H., Reddy, C., Sabharwal, A., Sellmann, M.: Snappy: a simple algorithm portfolio. In: International Conference on Theory and Applications of Satisfiability Testing, pp. 422–428. Springer (2013)

26.

Sim, K., Hart, E., Paechter, B.: A lifelong learning hyper-heuristic method for bin packing. Evol. Comput. 23(1), 37–67 (2015)CrossRef

27.

Soria-Alcaraz, J.A., Espinal, A., Sotelo-Figueroa, M.A.: Evolvability metric estimation by a parallel perceptron for on-line selection hyper-heuristics. IEEE Access 5, 7055–7063 (2017)CrossRef

28.

Soria-Alcaraz, J.A., Özcan, E., Swan, J., Kendall, G., Carpio, M.: Iterated local search using an add and delete hyper-heuristic for university course timetabling. Appl. Soft Comput. 40, 581–593 (2016)CrossRef

29.

Swan, J., Özcan, E., Kendall, G.: Hyperion—A Recursive Hyper-Heuristic Framework, pp. 616–630. Springer, Berlin (2011)

30.

Tsang, E., Voudouris, C.: Fast local search and guided local search and their application to british telecom’s workforce scheduling problem. Oper. Res. Lett. 20(3), 119–127 (1997)CrossRef

31.

Tyasnurita, R., Ozcan, E., John, R.: Learning heuristic selection using a time delay neural network for open vehicle routing. (2017)

32.

Van Onsem, W., Demoen, B.: Parhyflex: a framework for parallel hyper-heuristics. In: BNAIC 2013: Proceedings of the 25th Benelux Conference on Artificial Intelligence, Delft, The Netherlands, November 7-8, 2013. Delft University of Technology (TU Delft); under the auspices of the Benelux Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS), (2013)

33.

Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRef

34.

Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 32, 565–606 (2008)CrossRef

Titel: Deja Vu: a hyper heuristic framework with Record and Recall (2R) modules
Publikationsdatum: 21.08.2017
Erschienen in: Cluster Computing / Ausgabe Sonderheft 3/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1095-x

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