【正文】 gnises 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。 and is protected by a blood barrier capable of efficiently excluding nonself antigens from the thymic environment. Thus, most elements found within the thymus are representative of self instead of nonself. As an oute, the Tcells containing receptors capable of recognising these self antigens presented in the thymus are eliminated from the repertoire of Tcells through a process named negative selection. All Tcells that leave the thymus to circulate throughout the body are said to be tolerant to self, ., they do not respond to self.From an information processing perspective, negative selection presents an alternative paradigm to perform pattern recognition by storing information about the plement set (nonself) of the patterns to be recognised (self). A negative selection algorithm has been proposed in the literature with applications focused on the problem of anomaly detection, such as puter and network intrusion detection, time series prediction, image inspection and segmentation, and hardware fault tolerance. Given an appropriate problem representation (Section 3), define the set of patterns to be protected and call it the self set (P). Based upon the negative selection algorithm, generate a set of detectors (M) that will be responsible to identify all elements that do not belong to the selfset, ., the nonself elements. After generating the set of detectors (M), the next stage of the algorithm consists in monitoring the system for the presence of nonself patterns (Fig 2(b)). In this case, assume a set P* of patterns to be protected. This set might be posed of the set P plus other new patterns, or it can be a pletely novel set.For all elements of the detector set, that corresponds to the nonself patterns, check if it recognises (matches) an element of P* and, if yes, then a nonself pattern was recognized and an action has to be taken. The resulting action of detecting nonself varies according to the problem under evaluation and extrapolates the pattern recognition scope of this chapter. Clonal SelectionComplementary to the role of negative selection, clonal selection is the theory used to explain how an immune response is mounted when a nonself antigenic pattern is recognised by a Bcell. In brief, when a Bcell receptor recognises a nonself antigen with a certain affinity, it is selected to proliferate and produce antibodies in high volumes. The antibodies are soluble forms of the Bcell receptors that are released from the Bcell surface to cope with the invading nonself antigen. Antibodies bind to antigens leading to their eventual elimination by other immune cells. Proliferation in the case of immune cells is asexual, a mitotic process。 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. In plete opposition to the proliferation rate, the mutation suffered by each immune cell during reproduction is inversely proportional to the affinity of the cell receptor with the antigen: the higher the affinity, the smaller the mutation, and viceversa.Some authors have argued that a genetic algorithm without crossover is a reasonable model of clonal selection. However, the standard genetic 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)。2. For each pattern of P, present it to the population M and determine its affinity (match) with each element of the population M。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。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。6. Repeat Steps 2 to 5 until a certain criterion is met, such as a minimum pattern recognition or classification error.Note that this algorithm allows the artificial immune system to bee increasingly better at its task of recognising patterns (antigens). Thus, based upon