【正文】 works (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。 a process named clonal selection. In contrast, if a Bcell recognises a selfantigen, it might result in suppression, as proposed by the immune network theory. In the following subsections, each of these processes (negative selection, clonal selection, and network theory) will be described separately, along with their putational algorithms counterparts. Negative SelectionThe thymus is responsible for the maturation of Tcells。2. For each pattern of P, present it to the population M and determine its affinity (match) with each element of the population M。s1, s2,…,sL241。 clonal selection reproduces, maturates, and selects selfcells to recognise a set of nonself。 they adapt to the environment through a learning (or storage) algorithm, they can have their architectures dynamically adapted along with the weights, and they have the basic knowledge stored in the connection strengths.Component: The basic unit of an AIS is an attribute string s (along with its connections in network models) represented in the appropriate shapespace. This string s might correspond to an immune cell or molecule. In an ANN, the basic unit is an artificial neuron posed of an activation function, a summing junction, connection strengths, and an activation threshold. While artificial neurons are usually processing elements, attribute strings representing immune cells and molecules are information storage and processing ponents.Location of the ponents: In immune network models, the cells and molecules usually present a dynamic behaviour that tries to mimic or counteract the environment. This way, the network elements will be located according to the environmental stimuli. Unlike the immune network models, ANN have their neurons positioned in fixed predefined locations in the network. Some neural network models also adopt fixed neighbourhood patterns for the neurons. If a network pattern of connectivity is not adopted for the AIS, each individual element will have a position in the population that might vary dynamically. Also, a metadynamic process might allow the introduction and/or elimination of particular units.Structure: In negative and clonal AIS, the ponents are usually structured around matrices representing repertoires or populations of individuals. These matrices might have fixed or variable dimensions. In artificial immune networks and artificial neural networks, the ponents of the population are interconnected and structured around patterns of connectivity. Artificial immune networks usually have an architecture that follows the spatial distribution of the antigens represented in shapespace, while ANN usually have predefined architectures, and weights biased by the environment. Memory: The attribute strings representing the repertoire(s) of immune cells and molecules, and their respective numbers, constitute most of the knowledge contained in an artificial immune system. Furthermore, parameters like the affinity threshold can also be considered part of the memory of an AIS. In artificial immune network models, the connection strengths among units also carry endogenous and exogenous information, ., they quantify the interactions of the elements of the AIS themselves and also with the environment. In most cases, memory is contentaddressable and distributed. In the standa