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改进的克隆选择算法与SPEA相结合的进化算法
引用本文:杨观赐,马鑫,李少波,钟勇,于丽娅.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.
作者姓名:杨观赐  马鑫  李少波  钟勇  于丽娅
作者单位:1. 中国科学院成都计算机应用研究所,四川成都,610041
2. 贵州大学教育部现代制造技术重点实验室,贵州贵阳,550003
3. 中国科学院成都计算机应用研究所,四川成都610041/贵州大学教育部现代制造技术重点实验室,贵州贵阳550003
基金项目:教育部新世纪优秀人才支持计划;贵州省科学技术基金资助项目
摘    要:为了使进化过程中子代的繁殖能够像生物繁殖那样继承进化信息,通过挖掘抗体中优秀决定基并生成记忆集、增加高斯变异、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等策略,提出了一种改进的克隆选择算法(ICSA)。将ICSA与SPEA相结合,形成了一种改进的克隆选择算法与强度Pareto进化算法相结合的新型的进化算法(ICSA-SPEA)。ICSA-SPEA通过克隆选择替代选择、交叉、重组等遗传操作。用一组多目标0/1背包问题测试算法性能的统计结果表明,改进的算法可以有效保持种群多样性,具有良好的收敛精度与准确度。

关 键 词:多目标优化  进化算法  克隆选择  基因挖掘  遗传信息
收稿时间:2010/9/17 0:00:00
修稿时间:2011/1/16 0:00:00

Evolutionary Algorithm Based on Improved Clonal Selection Algorithm and SPEA
Yang Guanci,Ma Xin,Li Shaobo,Zhong Yong and Yu Liya.Evolutionary Algorithm Based on Improved Clonal Selection Algorithm and SPEA[J].Journal of Sichuan University (Engineering Science Edition),2011,43(5):109-113.
Authors:Yang Guanci  Ma Xin  Li Shaobo  Zhong Yong and Yu Liya
Institution:Chengdu Inst. of Computer Applications, Chinese Academy of Sciences,,,,
Abstract:In order to inherit evolutionary information as living beings during offspring generation, a kind of improved clonal selection algorithm (ICSA) was investigated by mining excellent gene schema to fill a memory pool from antibody set, applying Gaussian mutation operator, and replacing low affinity antibody with high affinity antibody with probability from mutation antibody population during updating memory antibody population. Combining ICSA with strength Pareto evolutionary algorithm (SPEA), a new kind of evolutionary algorithm based on ICSA and SPEA (ICSA-SPEA) was proposed, which replaces the genetic operation such as selection, crossover and recombinant with clonal selection. Taking multi-objective 0/1 knapsack problems to testing performance, the results show that ICSA-SPEA has ability to maintain the diversity of population, and is capable of finding out the well distributed non-dominated solutions approximating to Pareto front.
Keywords:Multiobjective Optimization  Evolutionary Algorithms  Clonal Selection  Genes Mining  Genetic Information
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