Abstract
Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for developing general-purpose automated solvers based on techniques from statistics and machine learning. In this paper, we push a step towards the development of a landscape-aware approach by proposing a set of landscape features for multi-objective combinatorial optimization, by decomposing the original multi-objective problem into a set of single-objective sub-problems. Based on a comprehensive set of bi-objective
and three variants of the state-of-the-art Moea/d algorithm, we study the association between the proposed features, the global properties of the considered landscapes, and algorithm performance. We also show that decomposition-based features can be integrated into an automated approach for predicting algorithm performance and selecting the most accurate one on blind instances. In particular, our study reveals that such a landscape-aware approach is substantially better than the single best solver computed over the three considered Moea/d variants.
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References
Beham, A., Wagner, S., Affenzeller, M.: Algorithm selection on generalized quadratic assignment problem landscapes. In: GECCO 2018, pp. 253–260 (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)
Grefenstette, J.J.: Predictive models using fitness distributions of genetic operators. Found. Genet. Algorithms 3, 139–161 (1995)
Kauffman, S.A.: The Origins of Order. Oxford University Press, Oxford (1993)
Kerschke, P., Trautmann, H.: The R-package FLACCO for exploratory landscape analysis with applications to multi-objective optimization problems. In: CEC, pp. 5262–5269 (2016)
Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27(1), 3–45 (2019)
Liefooghe, A., Daolio, F., Verel, S., Derbel, B., Aguirre, H., Tanaka, K.: Landscape-aware performance prediction for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 24(6), 1063–1077 (2020)
Liefooghe, A., Derbel, B., Verel, S., Aguirre, H., Tanaka, K.: Towards landscape-aware automatic algorithm configuration: preliminary experiments on neutral and rugged landscapes. In: Hu, B., López-Ibáñez, M. (eds.) EvoCOP 2017. LNCS, vol. 10197, pp. 215–232. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55453-2_15
Liefooghe, A., Verel, S., Derbel, B., Aguirre, H., Tanaka, K.: Dominance, indicator and decomposition based search for multi-objective QAP: landscape analysis and automated algorithm selection. In: Bäck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12269, pp. 33–47. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58112-1_3
Lindauer, M., van Rijn, J.N., Kotthoff, L.: The algorithm selection competition series 2015–17. CoRR abs/1805.01214 (2018)
Marquet, G., Derbel, B., Liefooghe, A., Talbi, E.-G.: Shake them all!. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 641–651. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_63
Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., Neumann, F.: A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann. Math. Artif. Intell. 69, 151–182 (2013). https://doi.org/10.1007/s10472-013-9341-2
Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)
Richter, H., Engelbrecht, A. (eds.): Recent Advances in the Theory and Application of Fitness Landscapes. Emergence Complexity and Computation. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-41888-4
Trivedi, A., Srinivasan, D., Sanyal, K., Ghosh, A.: A survey of multiobjective evolutionary algorithms based on decomposition. IEEE TEVC 21(3), 440–462 (2017)
Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: On the structure of multiobjective combinatorial search space: MNK-landscapes with correlated objectives. Eur. J. Oper. Res. 227(2), 331–342 (2013)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE TEVC 11, 712–731 (2008)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE TEVC 7(2), 117–132 (2003)
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Cosson, R., Derbel, B., Liefooghe, A., Aguirre, H., Tanaka, K., Zhang, Q. (2021). Decomposition-Based Multi-objective Landscape Features and Automated Algorithm Selection. In: Zarges, C., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2021. Lecture Notes in Computer Science(), vol 12692. Springer, Cham. https://doi.org/10.1007/978-3-030-72904-2_3
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