Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Scheduling

2026-03-09

AI Summary

This study addresses the uncertain agile Earth observation satellite scheduling problem (UAEOSSP), where uncertainties in reward, resource consumption, and target visibility often lead to suboptimal or infeasible schedules. To tackle this challenge, the authors propose a genetic programming hyper-heuristic (GPHH) framework embedded with a hybrid evaluation mechanism. This approach dynamically switches between exact and approximate evaluation modes during execution by integrating a constraint validation module with a simplified logical approximation, thereby balancing solution quality and computational efficiency. Experimental results across 16 simulated instances demonstrate that the proposed method significantly outperforms both hand-crafted heuristics and single-evaluation GPHH variants, achieving an average 17.77% reduction in training time and consistently attaining the best average ranking across diverse scenarios.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP) is a novel combinatorial optimization problem and a practical engineering challenge that aligns with the current demands of space technology development. It incorporates uncertainties in profit, resource consumption, and visibility, which may render pre-planned schedules suboptimal or even infeasible. Genetic Programming Hyper-Heuristic (GPHH) shows promise for evolving interpretable scheduling policies; however, their simulation-based evaluation incurs high computational costs. Moreover, the design of the constructive method, denoted as Online Scheduling Algorithm (OSA), directly affects fitness assessment, resulting in evaluation-dependent local optima within the policy space. To address these issues, this paper proposes a Hybrid Evaluation-based Genetic Programming (HE-GP) for effectively solving UAEOSSP. A Hybrid Evaluation (HE) mechanism is integrated into the policy-driven OSA, combining exact and approximate filtering modes: exact mode ensures evaluation accuracy through elaborately designed constraint verification modules, while approximate mode reduces computational overhead via simplified logic. HE-GP dynamically switches between evaluation models based on real-time evolutionary state information. Experiments on 16 simulated instance sets demonstrate that HE-GP significantly outperforms handcrafted heuristics and single-evaluation based GPHH, achieving substantial reductions in computational cost while maintaining excellent scheduling performance across diverse scenarios. Specifically, the average training time of HE-GP was reduced by 17.77\% compared to GP employing exclusively exact evaluation, while the optimal policy generated by HE-GP achieved the highest average ranks across all scenarios.

Problem

• Uncertain Agile Earth Observation Satellite Scheduling
• combinatorial optimization
• scheduling under uncertainty
• policy learning
• genetic programming

Innovation

• Hybrid Evaluation
• Genetic Programming Hyper-Heuristic
• Uncertain Agile Earth Observation Satellite Scheduling
• Online Scheduling Algorithm
• Dynamic Evaluation Switching