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[2602.15070] An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling
Summary
This paper presents a Genetic Programming Hyper-Heuristic (GPHH) designed for the Uncertain Agile Earth Observation Satellite Scheduling Problem, demonstrating improved scheduling efficiency under uncertainty compared to traditional heuristics.
Why It Matters
The research addresses the complexities of satellite scheduling in uncertain environments, which is crucial for optimizing resource use and operational efficiency in satellite missions. By automating scheduling policy generation, this work can significantly enhance the adaptability and performance of satellite operations.
Key Takeaways
- Introduces a novel approach to satellite scheduling under uncertainty.
- The Genetic Programming Hyper-Heuristic outperforms traditional heuristics.
- Achieves an average improvement of 5.03% over Look-Ahead Heuristics and 8.14% over Manually Designed Heuristics.
- Demonstrates real-time adaptability in scheduling policies.
- Highlights the importance of addressing uncertainty in satellite operations.
Computer Science > Neural and Evolutionary Computing arXiv:2602.15070 (cs) [Submitted on 15 Feb 2026] Title:An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling Authors:Yuning Chen, Junhua Xue, Wangqi Gu, Mingyan Shao View a PDF of the paper titled An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling, by Yuning Chen and 3 other authors View PDF Abstract:This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and task visibility) in order to reflect the reality that the actual information is inherently unknown beforehand. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can be utilized to adjust plans in real time and perform exceptionally well. Experimental results demonstrate that evolved scheduling policies significantly outperform both well-designed Look-Ahead Heuristics (LAHs) and Manually Designed Heuristics (MDHs). Specifically, the policies generated by GPHH achieve an average improvement of 5.03% compared to LAHs and 8.14% compared to MDHs. Comments: Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) MSC classes: 68T20 ACM classes: I.2.2 Cite as: arXiv:2602.15070 [cs.NE] ...