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Aiming at the problem of particle degradation and computational complexity in particle filter algorithm, an adaptive genetic particle filter (AGPF) algorithm is proposed. The algorithm uses the genetic algorithm instead of the resampling method in the traditional particle filter, and adaptively adjusts the number of particles in the filtering process according to the relationship between the number of particles and the variance of the filter error. The results show that the AGPF algorithm can reduce the number of filtered particles under the condition of ensuring the prediction accuracy, and is more suitable for the prediction of the performance of the rolling bearings.