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. This paper presents a GAbased method that produces general hyper-heuristics that solve two-dimensional cutting stock problems. The GA uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyper-heuristics, when tested with a large set of benchmark problems, produce outstanding results (optimal and near-optimal) 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.

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. This paper presents two Evolutionary-Computation-based Models to produce hyperheuristics that solve two-dimensional bin-packing problems. The first model uses an XCS-type Learning Classifier System which learns a solution procedure when solving individual problems. The second model is based on a GA that uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Both approaches, when tested and compared using a large set of benchmark problems, perform better than the combinations of single heuristics. The testbed is composed of problems used in other similar studies in the literature. Some additional instances of the testbed were randomly generated.