Abstract
Local search metaheuristics have been developed as a general tool for solving hard combinatorial search problems. However, in practice, metaheuristics very rarely work straight out of the box. An expert is frequently needed to experiment with an approach and tweak parameters, remodel the problem, and adjust search concepts to achieve a reasonably effective approach. Reactive search techniques aim to liberate the user from having to manually tweak all of the parameters of their approach. In this paper, we focus on one of the most well-known and widely used reactive techniques, reactive tabu search (RTS) [7], and propose a hyper-parameterized tabu search approach that dynamically adjusts key parameters of the search using a learned strategy. Experiments on MaxSAT show that this approach can lead to state-of-the-art performance without any expert user involvement, even when the metaheuristic knows nothing more about the underlying combinatorial problem than how to evaluate the objective function.
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References
Ansótegui, C., Bacchus, F., Järvisalo, M., Martins, R.: MaxSAT Evaluation (2017). http://mse17.cs.helsinki.fi
Ansotegui, C., Malitsky, Y., Samulowitz, H., Sellmann, M., Tierney, K.: Model-based geneticalgorithms for algorithm configuration. In: IJCAI, pp. 733–739 (2015)
Ansotegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: CP, pp. 142–157 (2009)
Ansótegui, C., Gabas, J., Malitsky, Y., Sellmann, M.: Maxsat by improved instance-specific algorithm configuration. Artif. Intell. 235, 26–39 (2016)
Ansótegui, C., Pon, J., Sellmann, M., Tierney, K.: Reactive dialectic search portfolios for maxsat. In: AAAI Conference on Artificial Intelligence (2017)
Argelich, J., Li, C., Manyà, F., Planes, J.: MaxSAT Evaluation (2016). www.maxsat.udl.cat
Battiti, R., Tecchiolli, G.: The reactive tabu search. ORSA J. Comput. 6(2), 126–140 (1994)
Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization, vol. 45. Springer Science & Business Media, Berlin (2008)
Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. Handbook of metaheuristics, pp. 457–474 (2003)
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)
Doerr, B., Doerr, C.: Optimal parameter choices through self-adjustment: Applying the 1/5-th rule in discrete settings. In: GECCO, pp. 1335–1342 (2015)
Doerr, B., Doerr, C.: Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))(1+(\lambda ,\lambda ))\)genetic algorithm. Algorithmica (2017)
Glover, F., Laguna, M.: Tabu search. In: Handbook of Combinatorial Optimization, pp. 3261–3362. Springer, Berlin (2013)
Glover, F., Laguna, M., Martí, R.: Principles of tabu search. In: Gonzalez, T. (ed.) Handbook of Approximation Algorithms and Metaheuristics (2007)
Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC-instance-specific algorithm configuration. In: Coelho, H., Studer, R., Wooldridge, M. (eds.) ECAI. FAIA, vol. 215, pp. 751–756 (2010)
Kadioglu, S., Sellmann, M.: Dialectic search. CP, pp. 486–500 (2009)
KhudaBukhsh, A., Xu, L., Hoos, H., Leyton-Brown, K.: SATenstein: automatically building local search sat solvers from components. In: IJCAI, pp. 517–524 (2009)
Leventhal, D., Sellmann, M.: The accuracy of search heuristics: an empirical study on knapsack problems. In: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 142–157 (2008)
Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: IJCAI, pp. 1542–1543 (2003)
Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm portfolios based on cost-sensitive hierarchical clustering. In: IJCAI, pp. 608–614 (2013)
Mısır, M., Verbeeck, K., De Causmaecker, P., Berghe, G.V.: An intelligent hyper-heuristic framework for chesc 2011. In: Learning and Intelligent Optimization, pp. 461–466. Springer, Berlin (2012)
Özcan, E., Mısır, M., Ochoa, G., Burke, E.K.: A reinforcement learning: great-deluge hyper-heuristic. In: Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends: Advancements and Trends, vol. 34 (2012)
Safarpour, S., Mangassarian, H., Veneris, A., Liffiton, M., Sakallah, K.: Improved design debugging using maximum satisfiability. In: Formal Methods in Computer Aided Design, pp. 13–19. IEEE (2007)
Sugawara, T.: Maxroster: solver description. In: MaxSAT Evaluation 2017, p. 12 (2017)
Vasquez, M., Hao, J.: A “logic-constrained” knapsack formulation and a tabu algorithm for the daily photograph scheduling of an earth observation satellite. Comput. Optim. Appl. 20(2), 137–157 (2001)
Xu, H., Rutenbar, R., Sakallah, K.: sub-SAT: a formulation for relaxed boolean satisfiability with applications in routing. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 22(6), 814–820 (2003)
Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: AAAI, pp. 210–216 (2010)
Acknowledgement
The authors would like to thank the Paderborn Center for Parallel Computation (PC\(^2\)) for the use of the OCuLUS cluster. This work was financially supported in part by TIN2016-76573-C2-2-P.
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Ansótegui, C., Heymann, B., Pon, J., Sellmann, M., Tierney, K. (2019). Hyper-Reactive Tabu Search for MaxSAT. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_27
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