【正文】 at the network in this case, is a network of affinities, which different from the ‘hardwired’ network of the nervous system. As a result of the network recognition events, it was suggested that the recognition of a cell receptor by another cell receptor results in network suppression, whilst the recognition of an antigen by a cell receptor results in network activation and cell proliferation. The original theory did not account explicitly for the results of network activation and/or suppression, and the various artificial immune networks found in the literature model it in a particular form.3 Modelling Pattern Recognition in AISUp to this point, the most relevant immune principles and their corresponding putational counterparts to perform pattern recognition have been presented. In order to apply these algorithms to putational problems, there is a need to specify a limited number of other aspects of artificial immune systems, not as yet covered. The first aspect to introduce is the most relevant representations to be applied to model self and nonself patterns. Here the selfpatterns correspond to the ponents of the AIS responsible for recognising the input patterns (nonself). Secondly, the mechanism by which the evaluation of the degree of match (affinity), or degree of recognition, of an input pattern by an element of the AIS has to be discussed. To model immune cells, molecules, and the antigenic patterns, the shapespace approach proposed is usually adopted. Although AIS model recognition through pattern matching, given certain affinity functions to be described further, performing pattern recognition through plementarity or similarity is based more on practical aspects than on biological plausibility. The shapespace approach proposes that an attribute string s = 225。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。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)。 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。 a process termed negative selection. If a Bcell encounters a nonself antigen with a sufficient affinity, it proliferates and differentiates into memory and effector cells。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 p