| 程序树层次化结构统计模型及MOSES改进算法 |
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| 引用本文： | 闻凌云,;刘贵全,;赵英海.程序树层次化结构统计模型及MOSES改进算法[J].北方交通大学学报,2009(6):132-136. |
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| 作者姓名： | 闻凌云 ;刘贵全 ;赵英海 |
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| 作者单位： | [1]中国科学技术大学计算机科学技术系,合肥230027; [2]中国科学技术大学电子工程与信息科学系,合肥230027 |
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| 摘 要： | 为提高MOSES效率，提出了一种新的程序树层次化结构统计模型．该模型通过统计分析同类群，自动发现子树特征来指导优化．该模型不需要hBOA算法那样对变量集合进行建模，也不需要像MRTS算法那样遍历小规模的种群来发现潜在的有指导意义的子树．通过解决人工蚂蚁问题对算法进行了测试，结果表明改进后的MOSES算法更加高效．
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| 关 键 词： | 自主程序演化 MOSES(语义进化搜索优化) 子树 人工蚂蚁问题 |
Hierarchical Statistical Structure Model of Program Trees and MOSES Algorithm Improvement |
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| Institution: | WEN Lingyun, LIU Guiquan, ZHAO Yinghai(1 Department of Computer Science and Technology; 2. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China) |
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| Abstract: | To improve the efficiency of MOSES algorithm, this paper proposes a new hierarchical statistical model of program trees. This model conducts hierarchical statistical analysis on program trees and can generate potential subtrees automatically to guide algorithm optimization. This model leaves out the operations of creating models for the variables set like the previous hBOA algorithm; and also doesn＇t need the tedious operations to traversal small population to find certain superior individuals as subtrees like the MRTS method. Experimental results on solving artificial ant problem indicate that our proposed algorithm is more effective and efficient than the previous hBOA-based MOSES. |
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| Keywords: | competent programming evolution meta-optimizing semantic evolutionary search（ MOESES） subtree artificial ant problem |
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