- author = "Zhiwen Miao and Chengran Lin",
- title = "Long Short Term Memory Autoencoder-aided Evolutionary Algorithm to Solve an Energy-Minimized Task Scheduling Problem",
- booktitle = "2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)",
- year = "2024",
- pages = "3083--3088",
- month = aug,
- keywords = "genetic algorithms, genetic programming, Schedules, Sequential analysis, Processor scheduling, Evolutionary computation, Numerical simulation, Scheduling, Numerical models, Resource management, Long short term memory, Radio spectrum management",
- ISSN = "2161-8089",
-
DOI = "
doi:10.1109/CASE59546.2024.10711571",
- abstract = "This paper addresses a task scheduling problem with deadline constraints in a human-cyber-physical system, which contains three subproblems, i.e., allocating processer, and determining tasks' sequence and frequency. To efficiently find its energy-efficient solutions in a short time, an autoencoder-aided evolutionary algorithm is proposed. The main optimiser chosen for it is genetic programming. To extract the implicit relationship among three strongly-coupled subproblems, a novel long short term memory autoencoder is built. In it, a group of long short term memory units are used to learn major features of decision variables and generate a low-dimensional hidden representation of a solution. After that, some network-aided mutation operators are designed to generate offsprings in the resulting low-dimensional space with informative features. Numerical experiments comparing the proposed method with several competitive methods verify the effectiveness of the proposed method in finding high-quality schedules in a reasonable time.",
-
notes = "Also known as \cite{10711571}
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China",
