Abstract
This paper presents an offline multi-objective hyperheuristic for the Modularised Fleet Mix Problem (MFMP) using Learning Classifier Systems (LCS). The LCS based hyperheuristic is built from multi-objective low-level heuristics that are derived from an existing MFMP solver. While the low-level heuristics use multi-objective evolutionary algorithms to search non-dominated solutions, the LCS based hyperheuristic applies the non-dominance concept at the primitive heuristic level. Two LCS, namely the eXtended Classifier System (XCS) and the sUpervised Classifier System (UCS) are augmented by multi-objective reward and accuracy functions, respectively. The results show that UCS performs better than XCS: the hyperheuristic learned by the UCS is able to select low-level heuristics which create MFMP solutions that, in terms of a distance-based convergence metric, are closer to the derived global Pareto curves on a large set of MFMP test scenarios than the solutions created by heuristics that are selected by the XCS hyperheuristic.
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© 2012 Springer-Verlag Berlin Heidelberg
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Shafi, K., Bender, A., Abbass, H.A. (2012). Multi Objective Learning Classifier Systems Based Hyperheuristics for Modularised Fleet Mix Problem. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_38
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DOI: https://doi.org/10.1007/978-3-642-34859-4_38
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