A novel evolutionary status guided hyper-heuristic algorithm for continuous optimization

This paper proposes a novel evolutionary status guided hyper-heuristic algorithm named ES-HHA for continuous optimization. A representative hyper-heuristic algorithm consists of two components: the low-level component and the high-level component. In the low-level component, to balance the exploitat...

Full description

Saved in:

Bibliographic Details
Published in:	Cluster computing Vol. 27; no. 9; pp. 12209 - 12238
Main Authors:	Zhong, Rui, Yu, Jun
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.12.2024 Springer Nature B.V
Subjects:	Ablation Computer Communication Networks Computer Science Design Design optimization Distance learning Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic Heuristic methods Mutation Operating Systems Operators (mathematics) Optimization techniques Performance evaluation Processor Architectures Statistical analysis Task complexity Hyper-heuristic algorithm Evolutionary status Low-level heuristics (LLHs) Fitness distance correlation (FDC) Population diversity (PD)
ISSN:	1386-7857, 1573-7543
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!