Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework

In many practical applications, usually, similar optimisation problems or scenarios repeatedly appear. Learning from previous problem-solving experiences can help adjust algorithm components of meta-heuristics, e.g., adaptively selecting promising search operators, to achieve better optimisation per...

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Veröffentlicht in:	arXiv.org 2024-04
Hauptverfasser:	Pei, Jiyuan, Liu, Jialin, Yi, Mei
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation Algorithms Benchmarks Combinatorial analysis Modules Operators Optimization Problem solving State-of-the-art reviews
Online-Zugang:	Volltext
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