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.
Sep 6, 2023
In this paper, a learning adaptive genetic algorithm (LAGA) is proposed for the Earth electromagnetic satellite scheduling problem (EESSP). Control parameters ...
Sep 6, 2023 · This study highlights the advantages of utilizing reinforcement learning to optimize neural network models for controlling genetic algorithm ...
Aug 8, 2024 · We propose a learning adaptive genetic algorithm (LAGA) for the earth electromagnetic satellite scheduling problem (EESSP). Control parameters ...
Abstract ; Publication: IEEE Transactions on Aerospace Electronic Systems ; Pub Date: December 2023 ; DOI: 10.1109/TAES.2023.3312626 ; Bibcode: 2023ITAES..59.9010S.
In the LAGA, a GRU artificial neural network model is used to control the parameters of variation operators and a policy gradient-based reinforcement ...
Jan 7, 2023 · We propose a learning adaptive genetic algorithm (LAGA) for the earth electromagnetic satellite scheduling problem (EESSP).
Learning Adaptive Genetic Algorithm for Earth Electromagnetic Satellite Scheduling. Yanjie Song 1. ,. Junwei Ou 2. ,. P. N. Suganthan 3. ,. Witold Pedrycz 4.
Learning Adaptive Genetic Algorithm for Earth Electromagnetic Satellite Scheduling. Y. Song, J. Ou, P. Suganthan, W. Pedrycz, Q. Yang, and L. Xing.
This paper proposes a mixed-integer programming model for the EDSSP problem and a genetic algorithm based on reinforcement learning (RL-GA).