結合基因與C-Means演算法則之向量量化器設計之研究
Abstract
本論文提出一個memetic algorithm (MA) 的演算法則來設計向量量化器。此演算法則使用steady-state genetic algorithm (GA) 做全域的搜尋，並採用C-Means演算法則進行局部的改善。與一般利用generational GA 做全域搜尋的MA比較之，本論文所提出的 MA 有效降低了vector quantization (VQ)訓練時的計算時間。除此之外，此演算法的結果接近全域最佳解，且對於初始的碼字選擇並不敏銳。最後的統計數據顯示，本論文所提出的steady-state MA與利用generational GA演算法執行全域搜尋的MA於相同基因族群個數的情況下，steady-state MA明顯的降低了CPU的計算時間。
A novel memetic algorithm (MA) for the design of vector quantizers (VQs) is presented in this paper. The algorithm uses steady-state genetic algorithm (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 reduce 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.
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 reduce 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.
Description
Keywords
向量量化器, 全域最佳解, Memetic algorithm, Vector quantizers, Steady-state genetic algorithm, C-Means algorithm