CBGA-ES+: A Cluster-Based Genetic Algorithm with Non-Dominated Elitist Selection for Supporting Multi-Objective Test Optimization
Author:
Funder
Norges Forskningsråd
RCN funded Zen-Configurator
RCN funded MBT4CPS
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Software
Link
http://xplorestaging.ieee.org/ielx7/32/9318455/08540431.pdf?arnumber=8540431
Cited by 21 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. Research on decomposition-based multi-objective evolutionary algorithm with dynamic weight vector;Journal of Computational Science;2024-09
2. A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem;Scientific Reports;2024-08-19
3. On Estimating the Feasible Solution Space of Multi-objective Testing Resource Allocation;ACM Transactions on Software Engineering and Methodology;2024-06-27
4. Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs;Frontiers in Human Neuroscience;2024-05-22
5. Prioritizing Natural Language Test Cases Based on Highly-Used Game Features;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30
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