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Theoretical and empirical analyses of Evolutionary Negative Selection Algorithms for a combinational optimization problem

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2 Author(s)
Xingxin Pei ; Nature Inspired Comput. & Applic. Lab., Univ. of Sci. & Technol. of China, Hefei, China ; Wenjian Luo

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.

Published in:

Bio-Inspired Computing, 2009. BIC-TA '09. Fourth International Conference on

Date of Conference:

16-19 Oct. 2009