采用GEP编码的克隆选择算法实现函数建模  

Clonal Selection Algorithm with GEP Code for Function Modeling

作　　者：莫海芳[1] 李康顺[2] 

机构地区：[1]中南民族大学计算与实验中心,武汉430074 [2]华南农业大学信息学院,广州510642

出　　处：《模式识别与人工智能》2013年第9期878-884,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60873114);国家留学基金项目;广东省科技攻关项目(No.2012A020602037)资助

摘　　要：克隆选择算法是通过选择优良个体并进行大量克隆,继而高频变异实现演化的.为选择优良个体,通常对种群按照个体的适应值进行排序.然而,GEP编码具有一个特点,即适应值相同的染色体,它们的编码不一定相同.如果按适应值进行排序时允许出现重复值,那么,当种群中出现多个相同的超级个体时,其将被超量克隆,使种群趋向单一.如果按适应值进行排序且不允许出现重复值,将会错失一些适应值相同但编码不同的优良个体,从而影响收敛速度.为保持种群的多样性,提高收敛速度,对克隆选择算法进行改进:选择若干个编码不同的优良个体进行克隆,即先对种群按照适应值进行降序排序;若适应值相同再比较其编码,相同编码的多个个体只保留一个.通过函数建模的若干实验表明,改进后的算法有较快的收敛速度.The clonal selection algorithm evolves through selecting best individuals, cloning the selected ones and hypermutation. The general method to find the best individuals is to sort the individuals according to their fitness. However, the GEP codes of those chromosomes with same fitness may be different. If duplicate individuals are allowed to appear in the sorted population, the duplicate superior individuals will be cloned excessively. In this case, the diversity of the population is decreased. If individuals are sorted just according to their fitness, the duplicate ones will be removed. And some best individuals with different codes may be abandoned. In order to maintain the diversity of population and increase the convergence rate, an improved elonal selection algorithm is proposed. Firstly, the individuals are sorted according to their fitness. Then, if there are multiple best individuals with same fitness, their codes are compared. The best individuals with different codes will be selected to clone. The experimental result shows that the proposed method maintains the diversity of population and increases the convergence rate.

关 键 词：函数建模 克隆选择 GEP编码 收敛速度 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]