Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework-Reference-Cited by-同舟云学术

Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework

Published:2022 Issue: Volume: Page:1-1
ISSN:1089-778X
Container-title:IEEE Transactions on Evolutionary Computation
language:
Short-container-title:IEEE Trans. Evol. Computat.

Author:

Yi Wenjie¹^ORCID,Qu Rong¹^ORCID,Jiao Licheng²^ORCID,Niu Ben³^ORCID

Affiliation:

1. Computational Optimisation and Learning (COL) Lab, School of Computer Science, University of Nottingham, United Kingdom

2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, China

3. College of Management, Shenzhen University, China

Funder

School of Computer Science, University of Nottingham

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/4235/4358751/09852781.pdf?arnumber=9852781

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems;Expert Systems with Applications;2024-12

2. On the automatic generation of metaheuristic algorithms for combinatorial optimization problems;European Journal of Operational Research;2024-11

3. A hybrid neural combinatorial optimization framework assisted by automated algorithm design;Complex & Intelligent Systems;2024-08-17

4. Continuous variation operator configuration for decomposition-based evolutionary multi-objective optimization;Swarm and Evolutionary Computation;2024-08

5. GRAHF: A Hyper-Heuristic Framework for Evolving Heterogeneous Island Model Topologies;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14