Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problem

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 producehyper-heuristics 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 alearning process which includes training and testing phases.Both approaches, when tested and compared using a large set ofbenchmark problems, perform better than the combinations ofsingle heuristics. The testbed is composed of problems used inother similar studies in the literature. Some additional instances of the testbed were randomly generated.