WASHH: An Anchor-Aware Whale-Guided Selection Hyper-Heuristic for Continuous Optimization and SVC Configuration
arXiv:2605.28844v1 Announce Type: cross Abstract: Learning-assisted algorithm design often has to make reliable search decisions under small evaluation budgets, where committing to a single metaheuristic can be unreliable. We propose WASHH, a Whale-guided Adaptive Selection Hyper-Heuristic for continuous black-box optimization. WASHH uses WOA as the main exploitation backbone, but treats PSO-style memory, GWO-style leader averaging, DE-style variation, local coordinate search, and anchor-guided refinement as selectable search behaviors. An online reward controller allocates evaluations accordi
This research addresses the ongoing challenge of reliable search decisions in learning-assisted algorithm design, proposing a novel hyper-heuristic that combines multiple metaheuristics to improve robustness.
Advanced optimization techniques are critical for improving the efficiency and robustness of AI systems across various applications, directly impacting their performance and reliability in complex environments.
The proposed WASHH algorithm offers a new approach to hyper-heuristic design for continuous black-box optimization, potentially leading to more reliable and efficient AI-driven solutions.
- · AI/ML researchers
- · Developers of autonomous systems
- · Industries relying on complex optimization
- · Computational science
- · Less robust, single-metaheuristic optimization approaches
Improved performance and stability in AI systems that rely on complex optimization problems.
Faster development cycles and deployment of AI solutions due to more efficient algorithm design.
Enhanced capabilities for AI agents to operate effectively in dynamic and unpredictable real-world environments.
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Read at arXiv cs.LG