J4 ›› 2011, Vol. 29 ›› Issue (03): 245-.

• 论文 • 上一篇    下一篇

基于多目标基因表达式编程的电路演化算法

吴 江1|唐常杰2|李太勇1|李自力1|刘洋洋1   

  1. 1 西南财经大学 |经济信息工程学院| 成都 |610074;2四川大学 |计算机学院| 成都 |610065
  • 出版日期:2011-05-20 发布日期:2011-06-27
  • 通讯作者: 吴江(1980— ),男,浙江衢州人,西南财经大学讲师,博士,主要从事数据库与知识工程研究,(Tel)86-13880950851(E-mail) E-mail:wuj_t@swufe.edu.cn。
  • 作者简介:吴江(1980— )|男|浙江衢州人|西南财经大学讲师|博士|主要从事数据库与知识工程研究|(Tel)86-13880950851(E-mail)wuj_t@swufe.edu.cn。
  • 基金资助:

    国家自然科学基金资助项目(60773169);“十一五”国家科技支撑计划基金资助项目(2006BAI05A01);西南财经大学“211工程”三期青年教师成长基金资助项目(211QN09071)

Evolutionary Algorithm of Circuit Based on Multi-Objective Gene Expression Programming

WU Jiang1|TANG Chang-jie2|LI Tai-yong1|LI Zi-li1|LIU Yang-yang1   

  1. 1School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 610074, China; 2School of Computer Science, Sichuan University, Chengdu 610065|China
  • Online:2011-05-20 Published:2011-06-27

摘要:

为提高电路演化的效率和成功率,对电路设计中涉及的多个目标进行了定义与量化,并针对多目标优化问题,在基因表达式编程(GEP:Gene Expression Programming)的基础上,提出了基于多目标基因表达式编程的电路演化算法(MGEP:MultiObjective Gene Expression Programming)。 设计了演化电路中的GEP编码,定义和量化了电路演化的多个目标,利用非支配排序和适应度共享策略提高搜索方向的空间均匀性。通过数字电路演化实验证明,MGEP算法与GP算法相比进化时间减少了72.9%,同时得到的电路更简单实用,得到最优电路的比率分别比GP和传统的GEP提高了50.4%和38.9%。

关键词: 演化硬件, 基因表达式编程, 演化算法, 多目标演化

Abstract:

Evolution of circuit is a focus of EHW(Evolvable Hardware). To improve the evolution efficiency and success rate of circuits, the multiobjectives during evolution of circuit are defined and quantized. To solve the multiobjective optimization, an evolutionary algorithm of circuits based on MGEP(MultiObjective Gene Expression Programming) is presented. The chromosome encoding of GEP(Gene Expression Programming) in evolution of circuit is designed; the multiobjectives during evolution of circuit are defined and quantized. And the uniformly scattered search direction is enhanced by nondominated sorting and fitness sharing strategy. The experiments on evolution of digital circuits show that MGEP improves the evolutionary efficiency. Compared with GP, the evolutionary time of MGEP drops 72.9%. MGEP is also capable of searching out simple and practical circuit. Compared with GP and GEP, the ratio of searching optimal circuit increases 50.4% and 38.9%.

Key words: evolvable hardware, gene expression programming (GEP), evolution algorithm, multi-objective evolution

中图分类号: 

  • TP301.6