Theoretical and Empirical Analyses of Evolutionary Negative Selection Algorithms for a Combinational Optimization Problem

被引：0

作者：

Pei, Xingxin ^{[1
]}

Luo, Wenjian ^{[1
]}

机构：

[1] Univ Sci & Technol China, Sch Comp Sci & Technol, Nat Inspired Computat & Applicat Lab, Hefei 230027, Anhui, Peoples R China

来源：

2009 FOURTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS, PROCEEDINGS | 2009年

关键词：

TIME; COMPLEXITY;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Evolutionary Negative Selection Algorithms (ENSAs) could be regarded as hybrid algorithms of Evolutionary Algorithms (EAs) and Negative Selection Algorithms (NSAs). The average time complexity of ENSAs on combinational optimization problems has never been studied before. In this paper, the average time complexity of ENSAs on one combinational optimization problem is analyzed. The theoretical results demonstrate that, for the Two Max function, the ENSA with an appropriate matching threshold could perform better than the traditional (N+N) EA. Some simulation experiments on the combinational problem are also done, and the experimental results are consistent with theoretical results.

引用

页码：270 / 277

页数：8

共 30 条

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