DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization

IF 11.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-05 DOI:10.1109/tevc.2024.3491155
Cheng Gong, Yang Nan, Ke Shang, Ping Guo, Hisao Ishibuchi, Qingfu Zhang
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DPP-HSS:为多目标优化实现快速、可扩展的超体积子集选择
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来源期刊
IEEE Transactions on Evolutionary Computation
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.
期刊最新文献
From Direct to Directional Variable Dependencies – Non-Symmetrical Dependencies Discovery in Real-World and Theoretical Problems Evolutionary Multitasking With Adaptive Knowledge Transfer for Expensive Multiobjective Optimization A Thompson Sampling-Based Sparse Evolutionary Operator for Sparse Large-Scale Multi-Objective Optimization DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking
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