- author = "Yuanzi Hong and Wei-Li Liu and Jinghui Zhong and Peng Liang and Jianhua Guo and Chunying Li",
- title = "Archive-based Cooperative Coevolution Genetic Programming for Workflow Scheduling",
- booktitle = "2024 IEEE Conference on Artificial Intelligence (CAI)",
- year = "2024",
- pages = "216--219",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Training, Processor scheduling, Heuristic algorithms, Sociology, Scheduling, Computational efficiency, Workflow scheduling, cooperative coevolution",
-
DOI = "
doi:10.1109/CAI59869.2024.00047",
- abstract = "Workflow Scheduling Problem (WSP) is a well-known combinatorial optimisation problem, which requires allocating tasks to available computing resources to maximize system efficiency, performance, or to meet specific requirements of service quality. The cooperative coevolution hyper-heuristic method based on genetic programming is a promising approach for addressing the WSP, attracting growing attention from researchers. However, this approach still faces the challenge of individual selection bias in the fitness evaluation when coevoluting two sub-populations. To address the above issue, this paper proposes an Archive-based Cooperative Coevolution GP (A-CCGP), which leverages an archive population to improve the quality of fitness evaluation. In addition, an adaptive mechanism is proposed to dynamically adjust the training set during the evolution to reduce the computational cost of fitness evaluation. Experimental results have validated the effectiveness of the proposed A-CCGP algorithm, in comparison with several state-of-the-art algorithms.",
-
notes = "Also known as \cite{10605573}
Guangdong Polytechnic Normal University, Guangzhou, China",
