摘要： 为维持进化过程中的种群多样性，并进一步提高求解问题的精确度，在SGP算法的基础上引入元胞自动机模型理论，提出一种能够实现具有细粒度并行的CSGP算法。该算法可提高求解问题的成功率以及减少进化代数，对比实验表明，CSGP算法较GEP算法和SGP算法在求解符号回归的问题上有较好的性能优势。
关键词:
线性遗传编程,
基因表达式编程,
元胞自动机,
符号回归
Abstract: For keeping various of population dynamics in the process of evolution and improving the definition of solution of problems, a fine particle parallel algorithm which called CSGP(Cellular Symbol Genetic Programming) is proposed based on the SGP(Symbol Genetic Programming) algorithm and cellular automaton model. This algorithm has higher success rate and need less computation time. Experiments as symbolic regression and so on are given. The result indicates that the GSCP algorithm has more efficiency and precisely in cost and precision than GEP and SGP.
Key words:
Linear Genetic Programming(LGP),
gene expression programming,
cellular automata,
symbolic regression
中图分类号:
周恺卿, 乐晓波, 潘小海, 莫礼平. 基于元胞自动机的线性遗传程序设计算法[J]. 计算机工程, 2011, 37(16): 161-163.
ZHOU Kai-Qing, LE Xiao-Bei, BO Xiao-Hai, MO Li-Beng. Linear Genetic Programming Algorithm Based on Cellular Automata[J]. Computer Engineering, 2011, 37(16): 161-163.