Abstract: In this paper, we propose a novel genetic algorithm approach to human motion capture (MoCap) data segmentation. For a given MoCap sequence, it constructs a symbolic representation through unsupervised sparse learning, detects the candidate segmenting points to the sequence, models the selection/deselection of each candidate with a gene, and employs the genetic algorithm to find the optimal solution. To the best of our knowledge, we for the first time introduce the genetic algorithm and the sparse learning technique to the problem of MoCap data segmentation, leading to excellent segmentation performance as experimentally demonstrated.