基于混合差异度控制的基因表达式编程

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摘要基因表达式编程(GEP)是一种进化算法，存在局部极小问题，解决此问题的一般方法是保持进化过程中种群的差异度。为了保证进化过程中种群的差异度，文中提出一种融合种群空间和样本空间的种群差异度度量方法。并基于此融合种群差异度度量方法，提出差异控制的GEP进化算法。同时在初始种群生成时，针对GEP结构的特殊性，将敌手理论应用于GEP种群初始化。实验结果表明文中算法能较有效避免过早陷入局部极小。

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	宣士斌
	刘怡光

关键词 ：基因表达式编程(GEP), 局部极小, 种群的差异度, 敌手理论, 融合种群差异度

Abstract：Gene expression programming (GEP) is an evolution algorithm which has the problem of local optimization like other evolution algorithms. The general method to this problem is to keep the diversity degree of population in the evolution. A method is proposed for measuring the diversity of the population, and it merges characters of both population space and sample space. Based on the method for mergence measuring the diversity of population, GEP evolution algorithm with diversity control is proposed. The rival theory is introduced into the initialization of population. The experimental results show that the proposed algorithm efficiently avoids falling into early local optimization.

Key words： Gene Expression Programming (GEP) Local Optimization Diversity of Population Rival Theory Fusing Diversity of Population

收稿日期: 2010-12-23

ZTFLH:

TP311

基金资助:国家自然科学基金项目(No.60705005)、四川省应用基础研究计划项目(No.2008JY0038)资助

作者简介: 宣士斌，男，1964年生，博士研究生，教授，主要研究方向为智能计算、模式识别、图像处理。E-mail:xshibin1997@126。com。刘怡光，男，1972年生，教授，博士生导师，主要研究方向为合成视觉、智能信息处理、模式识别。

引用本文:

宣士斌，刘怡光. 基于混合差异度控制的基因表达式编程[J]. 模式识别与人工智能, 2012, 25(2): 186-194. XUAN Shi-Bin, LIU Yi-Guang. GEP Evolution Algorithm Based on Control of Mixed Diversity Degree. , 2012, 25(2): 186-194.

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http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2012/V25/I2/186

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