【正文】 clonal selection reproduces, maturates, and selects selfcells to recognise a set of nonself。2. For each pattern of P, present it to the population M and determine its affinity (match) with each element of the population M。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 organized as follows. Section 2 describes relevant immune theories for pattern recognition and introduces their putational counterparts. In Section 3, we briefly describe how to model pattern recognition in artificial immune systems, and present a simple illustrative example. Section 4 contains a survey of AIS for pattern recognition, and Section 5 contrast the use of AIS with the use of ANN when applied to pattern recognition tasks. The chapter is concluded in Section 6.2 Biological and Artificial Immune SystemsAll living organisms are capable of presenting some type of defense against foreign attack. The evolution of species that resulted in the emergence of the vertebrates also led to the evolution of the immune system of this species. The vertebrate immune system is particularly interesting due to its several putational capabilities, as will be discussed throughout this section. The immune system of vertebrates is posed of a great variety of molecules, cells, and organs spread all over the body. There is no central organ controlling the functioning of the immune system, and there are several elements in transit and in different partments performing plementary roles. The main task of the immune system is to survey the organism in the search for malfunctioning cells from their own body (., cancer and tumour cells), and foreign disease causing elements (., viruses and bacteria). Every element that can be recognized by the immune system is called an antigen (Ag). The cells that originally belong to our body and are harmless to its functioning are termed self (or self antigens), while the disease causing elements are named nonself (or nonself antigens). The immune system, thus, has to be capable of distinguishing between what is self from what is nonself。3. Select n1 of the best highest affinity elements of M and generate copies of these individuals proportionally to their affinity with the antigen. The higher the affinity, the higher the number of copies, and viceversa。 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 tha