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An optimal feature subset selection method based on improved genetic algorithm (IGA) was presented. A novel scheme named segmented chromosome management was adopted in IGA. This scheme encodes the chromosome in binary as a whole while separates it logically into three segments for local management. These three segments are segment C for color feature, segment S for shape feature and segment T for texture feature separately. A segmented crossover operator and a segmented mutation operator are designed to operate on these segments to generate new chromosomes. These two operators avoid invalid chromosomes, thus improve the search efficiency extremely. The probabilities of crossover and mutation are adjusted automatically according to the generation number and the fitness value. By this way, the IGA could obtain strong search ability at the beginning of the evolution and achieve accelerated convergence along evolution. The experiment results indicate that IGA has stronger search ability and faster convergence speed than the simple genetic algorithm (SGA). The optimal feature subset that the IGA obtained has much smaller size than that of the SGA did, so it is more suitable for the online classification of foreign fibers.