Artificial Clonal Selection Model and Its Application

Artificial Clonal Selection Model and Its Application

Shangce Gao (University of Toyama, Japan), Zheng Tang (University of Toyama, Japan) and Hiroki Tamura (University of Miyazaki, Japan)
DOI: 10.4018/978-1-61520-757-2.ch006
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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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Introduction

Most living organisms exhibit extremely sophisticated learning and processing abilities that allow them to survive and proliferate generation after generation in their dynamic and competitive environments. For this reason, nature has always served as inspiration for several scientific and technological developments.

This area of research is often referred to as Biologically Inspired Computing. The motivation of this field is primarily to extract useful metaphors from natural biological systems, in order to create effective computational solutions to complex problems in a wide range of domain areas. The more notable developments have been the neural networks inspired by the working of the brain, and the evolutionary algorithms inspired by neo-Darwinian theory of evolution.

More recently however, there has been a growing interest in the use of the biological immune system as a source of inspiration to the development of these computational systems. The immune system contains many useful information-processing abilities, including pattern recognition, learning, memory and inherent distributed parallel processing. For these and other reasons, the immune system has received a significant amount of interest to use as a metaphor within computing. This emerging field of research is known as Artificial Immune Systems (AIS).

Essentially, AIS are the use of immune system components and process as inspiration to construct computational systems. The system is an emerging area of biologically inspired computation and has received a significant amount of interest from researchers and industrial sponsors in recent years. Applications of AIS include such areas as learning (Hunt & Cooke, 1996; Ichimura et.al, 2005; Nanni, 2006), fault diagnosis and fault tolerant (Canham, 2003; Branco, Dente, & Mendes, 2003), computer security and intrusion detection (Aickelin et.al., 2003; Dasgupta, 1999), and optimization (Engin & Doyen, 2004; Khilwani et.al, 2008). The field of AIS is showing great promise of being a powerful computing paradigm.

In this chapter, we further study the constructs and immune mechanism of natural immune system and present artificial immune systems based on the clonal selection principle. The mechanisms used in the algorithm are interpreted and several improvements of the algorithm are also introduced.

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