AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms With Diverse Structures

Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyon...

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Bibliographic Details
Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 9; no. 5; pp. 3690 - 3703
Main Authors:	Zhao, Qi, Yan, Bai, Hu, Taiwei, Chen, Xianglong, Yang, Jian, Cheng, Shi, Shi, Yuhui
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Algorithms automated algorithm design automated machine learning Automation Binary trees evolutionary algorithm Evolutionary computation Genetic algorithms Graph representations Graphical representations Heuristic algorithms Heuristic methods Metaheuristic Metaheuristics Optimization Performance enhancement Pipeline processing Problem-solving Prototypes Solvers Vectors
ISSN:	2471-285X, 2471-285X
Online Access:	Get full text
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