【正文】 t take into account other aspects, such as the number of patterns matched by each antibody.4 A Survey of AIS for Pattern RecognitionThe applications of artificial immune systems are vast, ranging from machine learning to robotic autonomous navigation. This section will review some of the works from the AIS literature applied to the pattern recognition domain. The rationale is to provide a guide to the literature and a brief description of the scope of applications of the algorithms. The section is divided into two parts for ease of prehension: 1) puter security, and 2) other applications. The problem of protecting puters (or networks of puters) from viruses, unauthorised users, etc., constitutes a rich field of research for pattern recognition systems. Due, mainly, to the appealing intuitive metaphor of building artificial immune systems to detect puter viruses, there has been a great interest from the puter science munity to this particular application. The use of the negative and clonal selection algorithms have been widely tested on this application. The former because it is an inherent anomaly (change) detection system, constituting a particular case of a pattern recognition device. The latter, the clonal selection algorithm, has been used in conjunction to negative selection due to its learning capabilities. Other more classical pattern recognition tasks, such as character recognition, and data analysis have also been studied within artificial immune systems. 5 AIS and ANN for Pattern RecognitionSimilar to the use of artificial neural networks, performing pattern recognition with an AIS usually involves three stages: 1) defining a representation for the patterns。 in our context, pattern recognition. If we recapitulate the three immune processes reviewed, negative selection, clonal selection, and immune network, all of them rely on a population M of individuals to recognise a set P of patterns. The negative selection algorithm has to define a set of detectors for nonself patterns。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)。 a process called self/nonself discrimination, and performed basically through pattern recognition events. From a pattern recognition perspective, the most appealing characteristic of the IS is the presence of receptor molecules, on the surface of immune cells, capable of recognising an almost limitless range of antigenic patterns. One can identify two major groups of immune cells, known as Bcells and Tcells. These two types of cells are rather similar, but differ with relation to how they recognise antigens and by their functional roles. Bcells are capable of recognising antigens free in solution (., in the blood stream), while Tcells require antigens to be presented by other accessory cells.Antigenic recognition is the first prerequisite for the immune system to be activated and to mount an immune response. The recognition has to satisfy some criteria. First, the cell receptor recognises an antigen with a certain affinity, and a binding between the receptor and the antigen occurs with strength proportional to this affinity. If the affinity is greater than a given threshold, named affinity threshold, then the immune system is activated. The nature of antigen, type of recognising cell, and the recognition site also influence the oute of an encounter between an antigen and a cell receptor.The human immune system contains an organ called thymus that is located behind the breastbone, which performs a crucial role in the maturation of Tcells. After Tcells are generated, they migrate into the thymus where they mature. During this maturation, all Tcells that recognise selfantigens are excluded from the population of Tcells。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。 2) adapting (learning or evolving) the system to identify a set of typical data。 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 shapespac