In various approaches for combinatorial optimization, a problem instance is represented by a numerical vector that summarizes to some extent the state of such instance. Such representation intends to include the most relevant features related to the instance and the problem domain. Previous approaches for hyper-heuristics have been relying in intuitive ways to determine the feature set. In this paper, a more general methodology for establishing an adequate problem-state representation is proposed. As an experimental environment to test the methodology, we employed the irregular case of the two-dimensional Bin Packing Problem (2D irregular BPP) and a GA-based solution model with training and testing phases. Our developed representation shows a significant improvement in performance with respect to a more conventional representation.
['Eunice López-Camacho', 'Hugo Terashima-Marín', 'Peter Ross', 'Manuel Valenzuela-Rendón']
Problem-state representations in a hyper-heuristic approach for the 2D irregular BPP