| 摘要: |
| 在基因表达式编程(gene expression programming,简称GEP)中,由于不同问题得到的适应度-距离相关系数(fitness-distance correlation,简称FDC)值很相近,所以难以用FDC 预测GEP 求解不同问题的进化难度.为了解决该问题,提出了态势模型及其区间密度指标来预测GEP的进化难度.主要工作包括:(1) 提出了GEP染色体之间的距离和态势模型的新概念;(2) 提出了态势模型中的区间密度指标;(3) 从动力学角度证明了态势模型是对GEP 原搜索空间的一种映射 |
| 关键词: 基因表达式编程(GEP) 进化难度 态势模型 区间密度 空间映射 |
| DOI：10.3724/SP.J.1001.2011.03768 |
| 分类号: |
| 基金项目:国家自然科学基金(60373000); 国家科技支撑计划(2006BAI05A01); 中国博士后科学基金(20090461346); 教育部人文社会科学研究青年基金(10YJCZH117); 中央高校基本科研业务费专项资金科技创新项目(SWJTU09CX035); 成都信息工程学院引进人才项目(KYTZ201110) |
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| Gene Expression Programming Evolution Difficulty Prediction Based on Posture Model |
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ZHENG Jiao-Ling1,2, TANG Chang-Jie1, XU Kai-Kuo1, YANG Ning1, DUAN Lei1, LI Hong-Jun1
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1.College of Computer Science, Sichuan University, Chengdu 610065, China;2.Department of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
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| Abstract: |
| Fitness Distance Correlation (FDC) can hardly predict the evolution difficulty of Gene Expression Programming (GEP) because problems with different hardness would result in very similar FDC values in GEP. To solve the problem, the authors propose a posture model and region density to predict GEP’s evolution difficulty. This study made the following contributions: (1) It introduces the concepts of the chromosomes’ distance and posture model in GEP; (2) It proposes region density of a posture model; (3) It proves that the posture model is a mapping from the original searching space, and the mapping preserves the population’s dynamic migration property in the original searching space; (4) It demonstrates the validity of using posture model and region density to predict GEP’s evolution difficulty; (5) It conducts extensive experiments to show that the new model can precisely predict the evolution difficulty of GEP. |
| Key words: gene expression programming (GEP) evolution difficulty posture model region density space mapping |
