Song, YanjieOu, JunweiSuganthan, Ponnuthurai NagaratnamPedrycz, WitoldYang, QinwenXing, Lining2024-05-192024-05-1920230018-92511557-9603https://doi.org10.1109/TAES.2023.3312626https://hdl.handle.net/20.500.12713/5555Earth electromagnetic exploration satellites are widely used in many fields due to their wide detection range and high detection sensitivity. The complex environment and the proliferating number of satellites make management a primary issue. In this article, a learning adaptive genetic algorithm (LAGA) is proposed for the Earth electromagnetic satellite scheduling problem (EESSP). Control parameters are essential to the successful performance of evolutionary algorithms, and their sensitivity to the problem makes tuning parameters very time-consuming. In the LAGA, a gated recurrent unit (GRU) neural network model is used to control the parameters of variation operators. The neural network model is capable of leveraging real-time information to achieve dynamic parameter adjustment during population search. Moreover, a policy gradient-based reinforcement learning method is utilized to update the parameters of GRU. An adaptive evolution mechanism is employed in LAGA for the autonomous selection of crossover operators. Additionally, the heuristic initialization method, elite strategy, and local search method are incorporated into LAGA to enhance overall performance. Simulation experiments demonstrate the effectiveness of LAGA in solving the EESSP. This study highlights the advantages of utilizing reinforcement learning to optimize neural network models for controlling genetic algorithm searches. Learning adaptive planning methods can effectively address complex problem scenarios and enhance satellite scheduling system performance.eninfo:eu-repo/semantics/closedAccessSatellitesGenetic AlgorithmsTask AnalysisReinforcement LearningApproximation AlgorithmsAdaptation ModelsSchedulingControl ParametersEarth Electromagnetic Satellite SchedulingGated Recurrent Unit (Gru)Genetic Algorithm (Ga)Learning AdaptiveReinforcement LearningArticle59690109025WOS:0011622640000032-s2.0-85171594662N/A10.1109/TAES.2023.3312626Q1