本文提出一種以改良式基因(memetic algorithm, MA)演算法來設計向量量化器(vector quantizers, VQs),此演算法是先用穩態型基因演算法(Steady-State genetic Algorithm, SSGA)來做全域搜尋,並再以C-Means演算法做局部改善,相較於其他改良式基因演算法使用世代型基因演算法(generational GA)去做全域搜尋。本文所提出的改良式基因演算法能有效降低向量量化器碼簿訓練時間,除此之外,其結果也最接近全域最佳解,且對初始的碼字選擇較不敏感。模擬結果顯示,本演算法在設計向量量化器上比其他改良式基因演算法在相同的基因族群個數下,擁有穩定效能且更能大幅降低CPU計算時間。
A novel memetic algorithm (MA) for the design of vector quantizers (VQs) is presented in this paper. The algorithm uses steady-state genetic algorithm (SSGA) 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.