Artificial Immune Optimization Algorithm

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.