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An Artificial Immune Dynamical System for Optimization

An Artificial Immune Dynamical System for Optimization

Licheng Jiao, Maoguo Gong, Wenping Ma
Copyright: © 2009 |Pages: 27
ISBN13: 9781605663104|ISBN10: 1605663107|EISBN13: 9781605663111
DOI: 10.4018/978-1-60566-310-4.ch003
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MLA

Jiao, Licheng, et al. "An Artificial Immune Dynamical System for Optimization." Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies, edited by Hongwei Mo, IGI Global, 2009, pp. 49-75. https://doi.org/10.4018/978-1-60566-310-4.ch003

APA

Jiao, L., Gong, M., & Ma, W. (2009). An Artificial Immune Dynamical System for Optimization. In H. Mo (Ed.), Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies (pp. 49-75). IGI Global. https://doi.org/10.4018/978-1-60566-310-4.ch003

Chicago

Jiao, Licheng, Maoguo Gong, and Wenping Ma. "An Artificial Immune Dynamical System for Optimization." In Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies, edited by Hongwei Mo, 49-75. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-310-4.ch003

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Abstract

Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection, positive selection, rather than the whole process of immune response to solve computational problems. In order to build a general computational framework by simulating immune response process, this chapter introduces a population-based artificial immune dynamical system, termed as PAIS, and applies it to numerical optimization problems. PAIS models the dynamic process of human immune response as a quaternion (G, I, R, Al), where G denotes exterior stimulus or antigen, I denotes the set of valid antibodies, R denotes the set of reaction rules describing the interactions between antibodies, and Al denotes the dynamic algorithm describing how the reaction rules are applied to antibody population. Some general descriptions of reaction rules, including the set of clonal selection rules and the set of immune memory rules are introduced in PAIS. Based on these reaction rules, a dynamic algorithm, termed as PAISA, is designed for numerical optimization. In order to validate the performance of PAISA, 9 benchmark functions with 20 to 10,000 dimensions and a practical optimization problem, optimal approximation of linear systems are solved by PAISA, successively. The experimental results indicate that PAISA has high performance in optimizing some benchmark functions and practical optimization problems.

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