Surrogate-Assisted Search With Competitive Knowledge Transfer for Expensive Optimization

IF 11.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Evolutionary Computation Pub Date : 2024-10-11 DOI:10.1109/tevc.2024.3478732

Xiaoming Xue, Yao Hu, Liang Feng, Kai Zhang, Linqi Song, Kay Chen Tan

引用次数: 0

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利用竞争性知识转移的代用辅助搜索实现高成本优化

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Evolutionary Computation 工程技术-计算机：理论方法

CiteScore

21.90

自引率

9.80%

发文量

196

审稿时长

3.6 months

期刊介绍： The IEEE Transactions on Evolutionary Computation is published by the IEEE Computational Intelligence Society on behalf of 13 societies: Circuits and Systems; Computer; Control Systems; Engineering in Medicine and Biology; Industrial Electronics; Industry Applications; Lasers and Electro-Optics; Oceanic Engineering; Power Engineering; Robotics and Automation; Signal Processing; Social Implications of Technology; and Systems, Man, and Cybernetics. The journal publishes original papers in evolutionary computation and related areas such as nature-inspired algorithms, population-based methods, optimization, and hybrid systems. It welcomes both purely theoretical papers and application papers that provide general insights into these areas of computation.