Key Principles in Cross-Domain Hyper-Heuristic Performance

• URL: http://arxiv.org/abs/2509.02782v1
• Date: Tue, 02 Sep 2025 19:36:28 GMT
• Title: Key Principles in Cross-Domain Hyper-Heuristic Performance
• Authors: Václav Sobotka, Lucas Kletzander, Nysret Musliu, Hana Rudová,
• Abstract summary: Cross-domain selection hyper-heuristics aim to distill decades of research on problem-specific search algorithms into general-purpose search strategies.<n>We focus on the composition of this set and its strategic transformations.<n>We demonstrate the raw effects of our transformations on a trivial unbiased random selection mechanism.
• Score: 3.9146761527401424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-domain selection hyper-heuristics aim to distill decades of research on problem-specific heuristic search algorithms into adaptable general-purpose search strategies. In this respect, existing selection hyper-heuristics primarily focus on an adaptive selection of low-level heuristics (LLHs) from a predefined set. In contrast, we concentrate on the composition of this set and its strategic transformations. We systematically analyze transformations based on three key principles: solution acceptance, LLH repetitions, and perturbation intensity, i.e., the proportion of a solution affected by a perturbative LLH. We demonstrate the raw effects of our transformations on a trivial unbiased random selection mechanism. With an appropriately constructed transformation, this trivial method outperforms all available state-of-the-art hyper-heuristics on three challenging real-world domains and finds 11 new best-known solutions. The same method is competitive with the winner of the CHeSC competition, commonly used as the standard cross-domain benchmark. Moreover, we accompany several recent hyper-heuristics with such strategic transformations. Using this approach, we outperform the current state-of-the-art methods on both the CHeSC benchmark and real-world domains while often simplifying their designs.

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