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

arXiv: https://arxiv.org/abs/2602.15070 · HTML: https://mlsanity.com/paper/2602.15070/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 15 Feb 2026.

## Builds on (most cited first, 1 of 1)

- [Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty](https://mlsanity.com/paper/2003.08363/) (2020)

## Built on by (most cited first, 2 of 2)

- [Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Scheduling](https://mlsanity.com/paper/2603.08447/) (2026)
- [Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites](https://mlsanity.com/paper/2608.24471/) (2026)

## Abstract (arXiv)

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.