| 多细胞基因表达式编程的函数优化算法 |
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| 引用本文： | 彭昱忠,元昌安,陈建伟,吴信东,王汝凉.多细胞基因表达式编程的函数优化算法[J].控制理论与应用,2010,27(11):1585-1589. |
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| 作者姓名： | 彭昱忠 元昌安 陈建伟 吴信东 王汝凉 |
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| 作者单位： | 1. 广西师范学院科学计算与智能信息处理广西高校重点实验室,广西南宁,530001
2. 美国佛蒙特大学计算机科学系,佛蒙特州05405 |
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| 基金项目： | 国家自然科学基金资助项目(60763012); 广西自然科学基金资助项目(桂科自0731028); 广西高等学校优秀人才资助计划资助项目(RC2007022). |
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| 摘 要： | 针对处理复杂的函数优化问题时传统演化算法易出现收敛性能不佳、搜索冗长和精度不高等问题,提出了一种基于多细胞基因表达式编程的函数优化新算法.该算法引入了同源基因和细胞系统思想,设计了相应新的个体编码方案、种群生成和遗传操作策略.通过对8个Benchmarks函数的对比实验,验证了该算法具有很强的全局寻优能力、较佳的收敛性能和更高的解精度.
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| 关 键 词： | 函数优化 演化算法 基因表达式编程 同源基因 细胞系统 |
| 收稿时间： | 2009/3/26 0:00:00 |
| 修稿时间： | 2010/3/15 0:00:00 |
Multicellular gene expression programming algorithm for function optimization |
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| PENG Yu-zhong,YUAN Chang-an,CHEN Jian-wei,WU Xin-dong and WANG Ru-liang.Multicellular gene expression programming algorithm for function optimization[J].Control Theory & Applications,2010,27(11):1585-1589. |
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| Authors: | PENG Yu-zhong YUAN Chang-an CHEN Jian-wei WU Xin-dong and WANG Ru-liang |
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| Affiliation: | Key Lab of Scientific Computing &Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University,Key Lab of Scientific Computing &Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University,Key Lab of Scientific Computing &Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University,Department of Computer Science, University of Vermont,Key Lab of Scientific Computing &Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University |
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| Abstract: | In dealing with complex function optimization problems, many existing evolutionary algorithms have performance limitations such as inability of convergence, poor searching efficiency and low precision. To cope with this problem, we adopt the idea of homeotic genes and cellular system, and propose a new algorithm based on multicell genetic expression programming. In addition, a new relevant individual coding method and new schemes of population generating and genetic operation are designed. Compared with other algorithms on eight Benchmark functions testing, the proposed algorithm shows higher precision, improved convergence ability and global search ability. |
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| Keywords: | function optimization evolutionary algorithm gene expression programming homeotic genes cellular system |
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