【正文】 netic algorithm does not account for important properties such as affinity proportional reproduction and mutation. Other authors proposed a clonal selection algorithm, named CLONALG, to fulfil these basic processes involved in clonal selection. This algorithm was initially proposed to perform pattern recognition and then adapted to solve multimodal optimisation tasks. Given a set of patterns to be recognised (P), the basic steps of the CLONALG algorithm are as follows:1. Randomly initialise a population of individuals (M)。 and the immune network maintains a set of individuals, connected as a network, to recognize self and nonself.Consider first the binary Hamming shapespace case, which is the most widely used. There are several expressions that can be employed in the determination of the degree of match or affinity between an element of P and an element of M. The simplest case is to simply calculate the Hamming distance (DH) between these two elements, as given by Eq. (1). Another approach is to search for a sequence of rcontiguous bits, and if the number of rcontiguous matches between the strings is greater than a given threshold, then recognition is said to have occurred. As the last approach to be mentioned here, we can describe the affinity measure of Hunt, given by Eq. (2). This last method has the advantage that it favours sequences of plementary matches, thus searching for similar regions between the attribute strings (patterns). （1） （2）where is the length of the ith sequence of matching bits longer than 2.In the case of Euclidean shapespaces, the Euclidean distance can be used to evaluate the affinity between any two ponents of the system. Other approaches such as the Manhattan distance may also be employed. Note that all the methods described rely basically, on determining the match between strings. However, there are AIS in the literature that take into account other aspects, such as the number of patterns matched by each antibody.4 A Survey of AIS for Pattern RecognitionThe applications of artificial immune systems are vast, ranging from machine learning to robotic autonomous navigation. This section will review some of the works from the AIS literature applied to the pattern recognition domain. The rationale is to provide a guide to the literature and a brief description of the scope of applications of the algorithms. The section is divided into two parts for ease of prehension: 1) puter security, and 2) other applications. The problem of protecting puters (or networks of puters) from viruses, unauthorised users, etc., constitutes a rich field of research for pattern recognition systems. Due, mainly, to the appealing intuitive metaphor of building artificial immune systems to detect puter viruses, there has been a great interest from the puter science munity to this particular application. The use of the negative and clonal selection algorithms have been widely tested on this application. The former because it is an inherent anomaly (change) detection system, constituting a particular case of a pattern recognition device. The latter, the clonal selection algorithm, has been used in conjunction to negative selection due to its learning capabilities. Other more classical pattern recognition tasks, such as character recognition, and data analysis have also been studied within artificial immune systems. 5 AIS and ANN for Pattern RecognitionSimilar to the use of artificial neural networks, performing pattern recognition with an AIS usually involves three stages: 1) defining a representation for the patterns。信息工程學(xué)院軟件工程Artificial Immune Systems:A Novel Paradigm to Pattern RecognitionAbstractThis chapter introduces a new putational intelligence paradigm to perform pattern recognition, named Artificial Immune Systems (AIS). AIS take inspiration from the immune system in order to build novel putational tools to solve problems in a vast range of domain areas. The basic immune theories used to explain how the immune system perform pattern recognition are described and their corresponding putational models are presented. This is followed with a survey from the literature of AIS applied to pattern recognition. The chapter is concluded with a tradeoff between AIS and artificial neural networks as pattern recognition paradigms.Keywords: Artificial Immune Systems；Negative Selection；Clonal Selection；Immune Network1 IntroductionThe vertebrate immune system (IS) is one of the most intricate bodily systems and its plexity is sometimes pared to that of the brain. With the advances in the biology and molecular genetics, the prehension of how the immune system behaves is increasing very rapidly. The knowledge about the IS functioning has unraveled several of its main operative mechanisms. These mechanisms have demonstrated to be very interesting not only from a biological standpoint, but also under a putational perspective. Similarly to the way the nervous system inspired the development of artificial neural networks (ANN), the immune system has now led to the emergence of artificial immune systems (AIS) as a novel putational intelligence paradigm. Artificial immune systems can be defined as abstract or metaphorical putational systems developed using ideas, theories, and ponents, extracted from the immune system. Most AIS aim at solving plex putational or engineering problems, such as pattern recognition, elimination, and optimization. This is a crucial distinction between AIS and theoretical immune system models. While the former is devoted primarily to puting, the latter is focused on the modeling of the IS in order to understand its behavior, so that contributions can be made to the biological sciences. It is not exclusive, however, the use of one approach into the other and, indeed, theoretical models of the IS have contributed to the development of AIS. This chapter is orga