Alejandro Sosa-Ascencio

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We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the(More)
The decomposition of problems into smaller elements is a widespread approach. In this paper we consider two approaches that are based over the principle to segmentation to problems for the resolution of resultant sub-components. On one hand, we have Automatically Defined Functions (ADFs), which originally emerged as a refinement of genetic programming for(More)
This article describes a grammar-based hyper-heuristic model for selecting heuristics to solve the two-dimensional bin packing problem (2D-PBB) with irregular pieces and regular objects. We propose to use a genetic programming approach to generate rules for selecting one suitable heuristic according to the features that characterize the problem state. The(More)
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