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| 基于差分进化基因表达式编程的全局函数优化 | |
| 引用本文： | 李太勇,唐常杰,吴江,邱江涛.基于差分进化基因表达式编程的全局函数优化[J].计算机科学,2009,36(11):140-142. |
| 作者姓名： | 李太勇 唐常杰 吴江 邱江涛 |
| 作者单位： | 1. 西南财经大学经济信息工程学院,成都,610074;四川大学计算机学院,成都,610065;西南财经大学中国支付体系研究中心,成都,610074 2. 四川大学计算机学院,成都,610065 3. 西南财经大学经济信息工程学院,成都,610074;西南财经大学中国支付体系研究中心,成都,610074 4. 西南财经大学经济信息工程学院,成都,610074 |
| 基金项目： | 国家自然科学基金，国家科技支撑计划重大项目，西南财经大学"211工程"三期青年教师成长项目 |
| 摘 要： | 为了提高基因表达式编程(Gene Expression Programming,GEP)在函数优化时的效率,将差分进化(Differ-ential Evolution,DE)引入到GEP中,提出了基于差分进化的基因表达式编程的全局优化算法DEGEPO.主要工作包括:(1)针对全局函数优化问题,根据GEP和DE的特点设计了新的基因编码;(2)设计了新的变异和交叉算子;(3)提出了DEGEPO算法并进行了算法分析;(4)实验验证了算法的有效性.相对于传统GEP,DEGEPO,优化结果精度平均提高了2～4个数量级. |
| 关 键 词： | 遗传算法 基因表达式编程 差分进化 函数优化 |
| 收稿时间： | 2008/12/24 0:00:00 |
| 修稿时间： | 3/7/2009 12:00:00 AM |
Global Function Optimization Based on Gene Expression Programming with Differential Evolution |
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| LI Tai-yong,TANG Chang-jie,WU Jiang,QIU Jiang-tao.Global Function Optimization Based on Gene Expression Programming with Differential Evolution[J].Computer Science,2009,36(11):140-142. | |
| Authors: | LI Tai-yong TANG Chang-jie WU Jiang QIU Jiang-tao |
| Affiliation: | (School of Economic Information Engineering,Southwest University of Finance and Economics,Chengdu 610074,China);(School of Computer Science,Sichuan University,Chengdu 610065,China);(The Research Center for China Payment System, Southwestern University of Finance and Economics, Chengdu 610074, China) |
| Abstract: | To improve the efficiency in function optimization via Gene Expression Programming(GEP),Differential Evolution(DE) was introduced into GEP. A novel algorithm called DEGEPO was proposed. The main work of this paper included (1) the gene in GEP was redesigned to adapt global function optimization; (2) novel mutation and crossover operations were applied; (3) a parameter optimization algorithm based on GEP with DE called DEGEPO was proposed and it was also analyzed; (4) experiments demonstrated the efficiency and effectiveness of DEGEPO. Compared with basic GEP, the precision of DEGEPO increased 2-4 orders of magnitude averagely. |
| Keywords: | Genetic algorithm(GA) Gene expression prograrnming(GEP) Differential evolution Function optimization |
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