Abstract
In various approaches for combinatorial optimization, a problem instance is represented by a numerical vector that summarizes to some extent the actual solution state of such instance. Such representation intends to include the most relevant features related to the instance and the problem domain. The proper selection of these features has a direct impact on the performance of a hyper-heuristic. Previous approaches for hyper-heuristics have been relying on intuitive ways to determine the feature set, usually based on the domain knowledge of a particular problem. In this paper, a more general methodology for establishing an adequate problem-state representation is proposed. We chose the irregular case of the two-dimensional Bin Packing Problem (2D irregular BPP) and a GA-based hyper-heuristic model to test the methodology. As far as we know, this is the only hyper-heuristic model applied to the 2D irregular BPP and it has been successful when solving a wide range of instances. Our developed representation shows a significant improvement in performance with respect to a more conventional representation for the 2D irregular BPP.
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López-Camacho, E., Terashima-Marín, H., Ross, P. (2010). Defining a Problem-State Representation with Data Mining within a Hyper-heuristic Model Which Solves 2D Irregular Bin Packing Problems. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_21
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DOI: https://doi.org/10.1007/978-3-642-16952-6_21
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