【正文】 for the maturation of Tcells。 the cells divide themselves (there is no crossover). During reproduction, the Bcell progenies (clones) undergo a hyper mutation process that, together with a strong selective pressure, result in Bcells with antigenic receptors presenting higher affinities with the selective antigen. This whole process of mutation and selection is known as the maturation of the immune response and is analogous to the natural selection of species. In addition to differentiating into antibody producing cells, the activated Bcells with high antigenic affinities are selected to bee memory cells with long life spans. These memory cells are preeminent in future responses to this same antigenic pattern, or a similar one.Other important features of clonal selection relevant from the viewpoint of putation are:1. An antigen selects several immune cells to proliferate. The proliferation rate of each immune cell is proportional to its affinity with the selective antigen: the higher the affinity, the higher the number of offspring generated, and viceversa。2. For each pattern of P, present it to the population M and determine its affinity (match) with each element of the population M。4. Mutate all these copies with a rate proportional to their affinity with the input pattern: the higher the affinity, the smaller the mutation rate, and viceversa.5. Add these mutated individuals to the population M and reselect n2 of these maturated (optimised) individuals to be kept as memories of the system。s1, s2,…,sL241。 SL), can represent any immune cell or molecule. Each attribute of this string is supposed to represent a feature of the immune cell or molecule, such as its charge, van der Wall interactions, etc. In the development of AIS the mapping from the attributes to their biological counterparts is usually not relevant. The type of attributes used to represent the string will define partially the shapespace under study, and is highly dependent on the problem domain. Any shapespace constructed from a finite alphabet of length k constitutes a kary Hamming shapespace. As an example, an attribute string built upon the set of binary elements {0,1} corresponds to a binary Hamming shapespace. It can be thought of, in this case, of a problem of recognising a set of characters represented by matrices posed of 0’s and 1’s. Each element of a matrix corresponds to a pixel in the character. If the elements of s are represented by realvalued vectors, then we have an Euclidean shapespace. Most of the AIS found in the literature employ binary Hamming or Euclidean shapespaces. Other types of shapespaces are also possible, such as symbolic shapespaces, which bine different (symbolic) attributes in the representation of a single string s. These are usually found in data mining applications, where the data might contain symbolic information like age, name, etc., of a set of patterns.Another important characteristic of the artificial immune systems is that most of them are population based. It means that they are posed of a set of individuals, representing immune cells and molecules, which have to perform a given role。 clonal selection reproduces, maturates, and selects selfcells to recognise a set of nonself。 2) adapting (learning or evolving) the system to identify a set of typical data。 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