Key Principles in Cross-Domain Hyper-Heuristic Performance

2025-09-02

AI Summary

This work addresses the limitation in cross-domain selection hyper-heuristics where low-level heuristics are statically predefined, lacking dynamic adaptation mechanisms. We propose a novel “set construction–strategy transformation” paradigm. Methodologically, we systematically model three principles—solution acceptance criteria, operator repetition constraints, and perturbation intensity control—and design a lightweight, randomized dynamic set construction and real-time transformation mechanism to enhance multiple modern hyper-heuristics. Our key contribution is the empirical revelation that a simple selection framework, when coupled with well-designed strategy transformations, can significantly outperform sophisticated adaptive methods—while achieving greater simplicity, interpretability, and generalizability. Experiments on three real-world combinatorial optimization problems yield 11 new best-known solutions; on the CHeSC benchmark, our approach comprehensively surpasses state-of-the-art methods, validating both the effectiveness and broad applicability of the proposed paradigm.

Technology Category

• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

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.

Problem

• Analyzing set composition and strategic transformations in hyper-heuristics
• Studying solution acceptance, LLH repetitions, and perturbation intensity principles
• Improving cross-domain performance and simplifying hyper-heuristic designs

Innovation

• Strategic transformation of low-level heuristics set
• Systematic analysis of acceptance, repetition, perturbation principles
• Trivial random selection outperforms state-of-the-art methods