重用抗体优良片断的免疫进化算法

上海交通大学学报（自然版） ›› 2012, Vol. 46 ›› Issue (02): 239-244.

• 自动化技术、计算机技术 • 上一篇下一篇

重用抗体优良片断的免疫进化算法

杨观赐1,2,马鑫1, 李少波1,2,钟勇2,于丽娅1

(1贵州大学教育部现代制造技术重点实验室，贵阳 550003;2中国科学院成都计算机应用研究所，成都 610041)

收稿日期:2010-12-25 出版日期:2012-02-28 发布日期:2012-02-28
基金资助:
教育部新世纪优秀人才支持计划资助项目(NCET090094), 国家高技术研究发展计划(863)项目 (2009AA043203), 贵州省科学技术基金资助项目(黔科合J字［2010］2095号)

Immune Evolutionary Algorithm Reusing Excellent Genes of Antibody

YANG Guan-Ci-1, 2 , MA Xin-1, LI Shao-Bo-1, 2 , ZHONG Yong-2, YU Li-Ya-1

(1 Key Laboratory of Advanced Manufacturing Technology of Ministry of Education,Guizhou University, Guiyang 550003, China; 2Chengdu Institute of Computer Applications,Chinese Academy of Sciences, Chengdu 610041, China)

Received:2010-12-25 Online:2012-02-28 Published:2012-02-28

摘要/Abstract

摘要： 基于克隆选择原理与算法,通过分析具体现象阐述了改进克隆选择算法的思想来源,设计了挖掘抗体中优秀决定基因并生成记忆集、封装优秀决定基片段、用变异抗体群中亲和度高的抗体按概率替换记忆抗体群中低亲和度抗体的方法，获得了重用抗体优良片断的克隆选择算法.借鉴强度Pareto进化算法的进化框架,提出了重用抗体优良片断的免疫进化算法.该算法通过克隆选择替代选择、交叉、重组等遗传操作.在一组0/1背包问题上的测试结果表明,所提出的算法可以有效保持种群多样性,获得较高质量的Pareto非劣解集.

关键词: 克隆选择, 强度帕雷托进化算法, 基因挖掘

Abstract: By expounding the ideological origin of improving the clonal selection algorithm through the analysis of the specific phenomenon, the method to extract excellent gene schema to fill a memory pool from antibody set, to package excellent gene segment, and to replace low affinity antibody with high affinity antibody with probability from mutation antibody population during updating memory antibody population was designed based on clonal selection principle and algorithm, and then an improved clonal selection algorithm reusing excellent gene segment was put forward. Refering to the framework of strength Pareto evolutionary algorithm, the immune evolutionary algorithm reusing excellent genes of antibody (RGIEM) was proposed, which implements the genetic operation such as selection, crossover and recombinant by applying the improved clonal selection algorithm. Taking a series of multiobjective 0/1 knapsack problems to check RGIEA’s performance, the results show that RGIEA is capable of maintaining the diversity of population and obtaining solutions approximating to Pareto front.

Key words: clonal selection, strength Pareto evolutionary algorithm (SPEA), gene mining

中图分类号:

TP 18
TP 273

杨观赐1, 2, 马鑫1, 李少波1, 2, 钟勇2, 于丽娅1. 重用抗体优良片断的免疫进化算法 [J]. 上海交通大学学报（自然版）, 2012, 46(02): 239-244.

YANG Guan-Ci-1, 2 , MA Xin-1, LI Shao-Bo-1, 2 , ZHONG Yong-2, YU Li-Ya-1. Immune Evolutionary Algorithm Reusing Excellent Genes of Antibody[J]. Journal of Shanghai Jiaotong University, 2012, 46(02): 239-244.

参考文献

［1］Sabatier L. Chromosomal instability［J］. Nature, 1992, 357: 548.

［2］公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究［J］.软件学报,2009,20(2):271289.

GONG Maoguo, JIAO Licheng, YANG Dongdong. Research on evolutionary multiobjective optimization algorithms［J］.

Journal of Software, 2009，20(2):271289.

［3］Lozano J A, ZHANG Qingfu, Larraaga P. Evolutionary algorithms based on probabilistic models ［J］.

IEEE Transactions on Evolutionary Computation, 2009, 13(6): 11971198.

［4］Karshenas H, Nikanjam A, Helmi B H, et al. Combinatorial effects of local structures and scoring metrics in bayesian optimization algorithm［C］//Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation.New York: ACM, 2009:263270

［5］姚金涛,林亚平,张明武.基于决策图贝叶斯网络的强度Pareto进化算法［J］.计算机学报,2005,28(12):19931999
YAO Jintao, LIN Yaping, ZHANG Mingwu. An improving strength Pareto evolutionary algorithm based on bayesian network with decision graphs ［J］.Chinese Journal of Computers, 2005, 28(12): 19931999

［6］Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjevtive optimization［C］//Proceedings of the Evolutionary Methods for Design, Optimization, and Control . Barcelona, Spain:CIMNE, 2002:95100

［7］杨观赐,马鑫,李少波,等.改进的克隆选择算法与SPEA相结合的进化算法［J］.四川大学学报:工程科学版, 2011, 43(5): 109113
YANG Guanci, MA Xin, LI Shaobo, et al. Evolutionary algorithm based on improved clonal selection algorithm and SPEA ［J］.

Journal of Sichuan University:Engineering Science Edition, 2011, 43(5): 109 113

［8］Cheng Z, Ventura M, She X W, et al. A genomewide comparison of recent chimpanzee and human segmental duplications ［J］. Nature, 2005, 437:8893

［9］HU Jianjun, Goodman E D, LI Shaobo, et al. Automated synthesis of mechanical vibration absorbers using genetic programming［J］. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2008, 22(3):207217

［10］Sunith B, Deb K. Automating discovery of innovative design principles through optimization［EB/OL］. (201002)［20100618］. http://www.iitk.ac.in/kangal/papers/k2010001pdf.

［11］YANG Guanci, LI Qin, LI Shaobo, et al. Sustainable evolutionary algorithm based on sequence mining［J］.

Journal of Computational Information Systems, 2011, 7(2):10271034

［12］Martins M B, Sabatier L, Ricoul M, et al. Specific chromosome instability induced by heavy ions: A step towards transformation of human fibroblasts［J］. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis,1993, 285(2): 229239

［13］李少波,杨观赐,郭观七. 基于相似性排挤与适应值分层计算的可持续Pareto遗传算法［J］.中国机械工程, 2007, 18(14):17171722
LI Shaobo,YANG Guanci, GUO Guanqi. Sustainable Pareto genetic algorithm based on similarity crowding and hierarchical fitness stratification［J］. China Mechanical Engineering,2007,18(14):17171722

[1]	雷菊阳. 基于中国餐馆过程的语音增强[J]. 上海交通大学学报（自然版）, 2013, 47(04): 635-639.
[2]	凌小峰, 宫新保, 金荣洪. 基于免疫机制的径向基函数网络动态训练方法[J]. 上海交通大学学报（自然版）, 2012, 46(04): 642-645.
[3]	孙旺, 李彦明, 杜文辽, 苑进, 刘成良. 基于蚁群神经网络的泵车主泵轴承性能评估[J]. 上海交通大学学报（自然版）, 2012, 46(04): 596-600.
[4]	张志英1, 杨克开1, 于瑾维2. 面向船体分段建造的二维不规则空间调度方法[J]. 上海交通大学学报（自然版）, 2012, 46(04): 651-656.
[5]	黄泽霞1, 2, 俞攸红3, 黄德才1. 惯性权自适应调整的量子粒子群优化算法 [J]. 上海交通大学学报（自然版）, 2012, 46(02): 228-232.

重用抗体优良片断的免疫进化算法

Immune Evolutionary Algorithm Reusing Excellent Genes of Antibody

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

编辑推荐

Metrics

本文评价