Artificial Clonal Selection Model and Its Application

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Author(s): Shangce Gao (University of Toyama, Japan), Zheng Tang (University of Toyama, Japan)and Hiroki Tamura (University of Miyazaki, Japan)
Copyright: 2010
Pages: 23
Source title: Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies
Source Author(s)/Editor(s): Leon Shyue-Liang Wang (National University of Kaohsiung, Taiwan)and Tzung-Pei Hong (National University of Kaohsiung, Taiwan)
DOI: 10.4018/978-1-61520-757-2.ch006

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Artificial Immune System as a new branch in computational intelligence is the distributed computational technique inspired by immunological principles. In particular, the Clonal Selection Algorithm (CS), which tries to imitate the mechanisms in the clonal selection principle proposed by Burent to better understand its natural processes and simulate its dynamical behavior in the presence of antigens, has received a rapid increasing interest. However, the description about the mechanisms in the algorithm is rarely seen in the literature and the related operators in the algorithm are still inefficient. In addition, the comparison with other algorithms (especially the genetic algorithms) lacks of analysis. In this chapter, several new clonal selection principles and operators are introduced, aiming not only at a better understanding of the immune system, but also at solving engineering problems more efficiently. The efficiency of the proposed algorithm is verified by applying it to the famous traveling salesman problems (TSP).

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