hyper-heuristics and classifier systems for solving 2d-regular cutting stock problems
This paper presents a method for combining concepts of Hyper-heuristics and Learning Classifier Systems for solving 2D Cutting Stock Problems. The idea behind Hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. In this paper, the Hyper-heuristic is formed using a XCS-type Learning Classifier System which learns a solution procedure when solving individual problems. The XCS evolves a behavior model which determines the possible actions (selection and placement heuristics) for given states of the problem. When tested with a collection of different problems, the method finds very competitive results for most of the cases. The testebed is composed of problems used in other similar studies in the literature. Some additional instances of the testbed were randomly generated.
hyper-heuristics classifier systems cutting stock evolutionary computation optimization