A Superior Vector Quantization Based on Steady-State Memetic Algorithm

A novel memetic algorithm (MA) for the design of vector quantizers (VQs) is presented in this paper. The algorithm uses steady-state genetic algorithm (GA) for the global search and C-means algorithm for the local improvement. As compared with the usual MA using the generational GA for global search, the proposed MA effectively reduces the computational time for VQ training. In addition, it attains near global optimal solution, and its performance is insensitive to the selection of initial codewords. Numerical results show that the proposed algorithm has significantly lower CPU time over other MA counterparts running on the same genetic population size for VQ design.