The faster higher-order cellular automaton for hyper-parallel undistorted data compression

This paper defines second-order and third-order permutation global functions and presents the corresponding higher-order cellular automaton approach to the hyper-parallel undistorted data compression.The genetic algorithm is successfully devoted to finding out all the correct local compression rules for the higher-order cellualr automaton.The correctness of the higher-order compression rules,the time complexity,and the systolic hardware implementation issue are discussed.In comparison with the first-order automation method reported,the proposed higher-order approach has much faster compression speed with almost the same degree of cellular structure complexity for hardware implementation.     

基于混沌优化技术的并行进化模型及性能分析

提出了基于混沌优化技术的并行免疫量子进化算法,算法中将种群分成若干个独立的子群体,称为宇宙.宇宙内采用免疫量子进化算法、灾变算子,宇宙间采用基于学习机制的移民、基于混沌序列的信息交互,因此算法具有更好的种群多样性、更快的收敛速度和全局寻优能力.不仅从理论上分析了算法的性能,而且通过仿真实验验证了该算法的优越性.