An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling

Yuning Chen, Junhua Xue, Wangqi Gu, Mingyan Shao
2/15/2026
cs.NEcs.AI

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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.

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Cite this paper

@article{chen2026effective,
  title  = {An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling},
  author = {Yuning Chen and Junhua Xue and Wangqi Gu and Mingyan Shao},
  year   = {2026},
  eprint = {2602.15070},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.15070v1}
}

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