Abstract
The relation between problem and solution algorithm presents a similar phenomenon in different research problems (optimization, decision, classification, ordering); the algorithm performance is very good in some cases of the problem, and very bad in other. Majority of related works have worked for predicting the most adequate algorithm to solve a new problem instance. However, the relation between problem and algorithm is not understood at all. In this paper a formal characterization of this relation is proposed to facilitate the analysis and understanding of the phenomenon. Case studies for Tabu Search algorithm and One Dimension Bin Packing problem were performed, considering three important sections of algorithm logical structure. Significant variables of problem structure and algorithm searching behavior from past experiments, metrics known by scientific community were considered (Autocorrelation Coefficient and Length) and significant variables of algorithm operative behavior were proposed. The models discovered in the case studies gave guidelines that permits to redesign algorithm logical structure, which outperforms to the original algorithm in an average of 69%. The proposed characterization for the relation problem-algorithm could be a formal procedure for obtaining guidelines that improves the algorithm performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Garey, M.R., Jhonson, D.S.: Computers and Intractability, a Guide to the Theory of NP-completeness. W. H. Freeman and Company, New York (1979)
Papadimitriou, C., Steiglitz, K.: Combinatorial Optimization, Algorithms and Complexity. Prentice Hall, Upper Saddle River (1982)
Rendell, L., Cho, H.: Empirical learning as a function of concept character. Mach. Learn. 5, 267–298 (1990)
Lagoudakis, M., Littman, M.: Learning to select branching rules in the DPLL procedure for satisfiability. Electron. Notes Discrete Math. 9, 344–359 (2001)
Smith-Miles, K.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 1–25 (2009)
Wolpert, D., Macready, W.: No free lunch theorems for optimizations. IEEE Trans. Evol. Comput. 1(1), 67–82 (1996)
Vanchipura, R., Sridharan, R.: Development and analysis of constructive heuristic algorithms for flow shop scheduling problems with sequence-dependent setup times. Int. J. Adv. Manuf. Technol. 67, 1337–1353 (2013)
Hutter, F., Xu, L., Hoos, H., Leyton-Brown, K.: Algorithm runtime prediction: methods & evaluation. Artif. Intell. 206, 79–111 (2014)
Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: Proceedings of the 25th National Conference on Artificial Intelligence (AAAI 2010), pp. 210–216 (2010)
Cayci, A., Menasalvas, E., Saygin, Y., Eibe, S.: Self-configuring data mining for ubiquitous computing. Inf. Sci. 246, 83–99 (2013)
Pavón, R., Díaz, F., Laza, R., Luzón, M.: Experimental evaluation of an automatic parameter setting system. Expert Syst. Appl. 37, 5224–5238 (2010)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_40
Yeguas, E., Luzón, M., Pavón, R., Laza, R., Arroyo, G., Díaz, F.: Automatic parameter tuning for evolutionary algorithms using a Bayesian case-based reasoning system. Appl. Soft Comput. 18, 185–195 (2014)
Ries, J., Beullens, P.: A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction. J. Oper. Res. Soc. 66(5), 782–793 (2015)
Yong, X., Feng, D., Rongchun, Z.: Optimal selection of image segmentation algorithms based on performance prediction. In: Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing, pp. 105–108. Australian Computer Society, Inc. (2003)
Pérez, J., Pazos, R.A., Frausto, J., Rodríguez, G., Romero, D., Cruz, L.: A statistical approach for algorithm selection. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 417–431. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24838-5_31
Nikolić, M., Marić, F., Janičić, P.: Instance-based selection of policies for SAT solvers. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 326–340. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02777-2_31
Yuen, S., Zhang, X.: Multiobjective evolutionary algorithm portfolio: choosing suitable algorithm for multiobjective optimization problem. In: 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China, pp. 1967–1973 (2014)
Munoz, M., Kirley, M., Halgamuge, S.: Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Trans. Evol. Comput. 19(1), 74–87 (2015)
Leyton-Brown, K., Hoos, H., Hutter, F., Xu, L.: Understanding the empirical hardness of np-complete problems. Mag. Commun. ACM 57(5), 98–107 (2014)
Cruz, L., Gómez, C., Pérez, J., Landero, V., Quiroz, M., Ochoa, A.: Algorithm Selection: From Meta-Learning to Hyper-Heuristics. INTECH Open Access Publisher (2012)
Wagner, M., Lindauer, M., Misir, M., et al.: A case of study of algorithm selection for the travelling thief problem. J. Heuristics, 1–26 (2017)
Pérez, J., Cruz, L., Landero, V.: Explaining performance of the threshold accepting algorithm for the bin packing problem: a causal approach. Pol. J. Environ. Stud. 16(5B), 72–76 (2007)
Tavares, J.: Multidimensional knapsack problem: a fitness landscape analysis. IEEE Trans. Syst. Man Cybern. Part B Cybern. 38(3), 604–616 (2008)
Pérez, J., et al.: An application of causality for representing and providing formal explanations about the behavior of the threshold accepting algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 1087–1098. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69731-2_102
Smith-Miles, K., van Hemert, J., Lim, X.Y.: Understanding TSP difficulty by learning from evolved instances. In: Blum, C., Battiti, R. (eds.) LION 2010. LNCS, vol. 6073, pp. 266–280. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13800-3_29
Quiroz, M., Cruz, L., Torrez, J., Gómez, C.: Improving the performance of heuristic algorithms based on exploratory data analysis. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems, Studies in Computational Intelligence, vol. 452, pp. 361–375. Springer, Heidelberg (2013)
Landero, V., Pérez, J., Cruz, L., Turrubiates, T., Rios, D.: Effects in the algorithm performance from problem structure, searching behavior and temperature: a causal study case for threshold accepting and bin-packing problem. In: Misra, S., Gervasi, O., Murgante, B. (eds.) ICCSA 2019. LNCS, vol. 11619, pp. 152–166. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-24289-3_13
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. The MIT Press, Cambridge (2001)
Beasley, J., E.: OR-Library. Brunel University (2006). http://people.brunel.ac.uk/~mastjjb/jeb/orlib/binpackinfo.html
Scholl, A., Klein, R.: (2003). http://www.wiwi.uni-jena.de/Entscheidung/binpp/
Glover, F.: Tabu search - Part I, first comprehensive description of tabu search. ORSA-J. Comput. 1(3), 190–206 (1989)
Fleszar, K., Hindi, K.S.: New heuristics for one-dimensional bin packing. Comput. Oper. Res. 29, 821–839 (2002)
Khuri, S., Schütz, M., Heitkötter, J.: Evolutionary heuristics for the bin packing problem. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna (1995). https://doi.org/10.1007/978-3-7091-7535-4_75
Merz, P., Freisleben, B.: Fitness landscapes and memetic algorithm design. In: New Ideas in Optimization, pp. 245–260. McGraw-Hill Ltd., UK (1999)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI, pp. 1022–1029 (1993)
Hall, M.A.: Feature selection for discrete and numeric class machine learning (1999)
Watson, J., Darrell, W., Adele, E.: Linking search space structure, run-time dynamics, and problem difficulty: a step toward demystifying tabu search. J. Artif. Intell. Res. 24, 221–261 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Landero, V., Ríos, D., Pérez, J., Cruz, L., Collazos-Morales, C. (2020). Characterizing and Analyzing the Relation Between Bin-Packing Problem and Tabu Search Algorithm. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-58799-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58798-7
Online ISBN: 978-3-030-58799-4
eBook Packages: Computer ScienceComputer Science (R0)