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Artificial Immune Optimization Algorithm

Artificial Immune Optimization Algorithm

Waseem Ahmad
Copyright: © 2015 |Pages: 20
ISBN13: 9781466685130|ISBN10: 1466685131|EISBN13: 9781466685147
DOI: 10.4018/978-1-4666-8513-0.ch006
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MLA

Ahmad, Waseem. "Artificial Immune Optimization Algorithm." Improving Knowledge Discovery through the Integration of Data Mining Techniques, edited by Muhammad Usman, IGI Global, 2015, pp. 104-123. https://doi.org/10.4018/978-1-4666-8513-0.ch006

APA

Ahmad, W. (2015). Artificial Immune Optimization Algorithm. In M. Usman (Ed.), Improving Knowledge Discovery through the Integration of Data Mining Techniques (pp. 104-123). IGI Global. https://doi.org/10.4018/978-1-4666-8513-0.ch006

Chicago

Ahmad, Waseem. "Artificial Immune Optimization Algorithm." In Improving Knowledge Discovery through the Integration of Data Mining Techniques, edited by Muhammad Usman, 104-123. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8513-0.ch006

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Abstract

Artificial immune system (AIS) is a paradigm inspired by processes and metaphors of natural immune system (NIS). There is a rapidly growing interest in AIS approaches to machine learning and especially in the domain of optimization. Of particular interest is the way human body responds to diseases and pathogens as well as adapts to remain immune for long periods after a disease has been combated. In this chapter, we are presenting a novel multilayered natural immune system (NIS) inspired algorithms in the domain of optimization. The proposed algorithm uses natural immune system components such as B-cells, Memory cells and Antibodies; and processes such as negative clonal selection and affinity maturation to find multiple local optimum points. Another benefit this algorithm presents is the presence of immunological memory that is in the form of specific memory cells which keep track of previously explored solutions. The algorithm is evaluated on two well-known numeric functions to demonstrate the applicability.

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