Dual-Tree Genetic Programming for Automated Discovery of Computing Power Network Scheduling Heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{DBLP:conf/cec/ZhaoJXLGWW25,

• author = "Benjie Zhao and Ruwang Jiao and Meng Xu and Shuaishuai Liu and Chao Guo and Shaolin Wang and Jin Wang",
• title = "Dual-Tree Genetic Programming for Automated Discovery of Computing Power Network Scheduling Heuristics",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Sequential analysis, Uncertainty, Processor scheduling, Simulation, Graphics processing units, GPU, Spread spectrum communication, Real-time systems, Heterogeneous networks, Servers, computing power network, dynamic scheduling",
• isbn13 = "979-8-3315-3432-5",
• timestamp = "Mon, 30 Jun 2025 01:00:00 +0200",
• biburl = "https://dblp.org/rec/conf/cec/ZhaoJXLGWW25.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/CEC65147.2025.11042945",
• DOI = "10.1109/CEC65147.2025.11042945",
• abstract = "The computing power network links distributed and heterogeneous computing resources via the network, to enable efficient configuration and use of computing power. However, scheduling computing resources within this network presents several challenges, such as resource heterogeneity, vast search spaces, uncertainty, high constraints, and real-time requirements. To simulate the real-world computing power network scheduling problem, this paper integrates cloud servers, fog servers, and edge servers into a unified computing power network, considering their respective GPU, CPU, and bandwidth resources. We introduce a Dual-Tree Genetic Programming (DTGP) approach that simultaneously optimises two critical decisions--routing and sequencing--to automatically evolve computing power network scheduling heuristics for real-time decision-making. Additionally, to improve the performance of DTGP, we propose new terminal sets tailored to fit within these two GP trees. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in six test scenarios, achieving up to 40percent reduction in completion time.",
• notes = "also known as \cite{zhao:2025:CEC2} \cite{11042945}",

}

Genetic Programming entries for Benjie Zhao Ruwang Jiao Meng Xu Shuaishuai Liu Chao Guo Shaolin Wang Jin Wang

Citations