How Much Is Too Much? Facing Practical Limitations in Hyper-Heuristic Design for Packing Problems

Hyper-heuristics, or simply heuristics to choose heuristics, represent a powerful approach to tackling complex optimization problems. These methods decide which heuristic to apply throughout the solving process, aiming to improve the solving process. While they have demonstrated significant success across various domains, their suitability for all problem instances, even within a specific domain, is not guaranteed. The literature provides many examples of successful hyper-heuristic models for packing problems. Among those models, we can mention rule-based and fixed-sequence-based hyper-heuristics. These two models have proven useful in various scenarios. This paper investigates a genetic-based approach that produces hybrid hyper-heuristics. Such hybrid hyper-heuristics combine rule-based decisions while firing heuristic sequences. The rationale behind this hybrid approach is that we aimed to combine the strengths of both approaches. Although we expected to improve on the individual performance of the methods, we obtained contradictory results that suggest that, at least in this work, combining the strengths of different hyper-heuristic models may not be a suitable approach.