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«Artificial Immune Systems: A Novel Paradigm to Pattern Recognition L. N. de Castro and J. Timmis Computing Laboratory University of Kent at ...»

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In Artificial Neural Networks in Pattern Recognition, J. M. Corchado, L. Alonso, and

C. Fyfe (eds.), SOCO-2002, University of Paisley, UK, pp. 67-84, 2002.

Artificial Immune Systems:

A Novel Paradigm to Pattern Recognition

L. N. de Castro and J. Timmis

Computing Laboratory

University of Kent at Canterbury

Kent, Canterbury, CT2 7NF, United Kingdom

e-mail: {L.N.deCastro, J.Timmis}@ukc.ac.uk


This chapter introduces a new computational intelligence paradigm to perform pattern recognition, named Artificial Immune Systems (AIS). AIS take inspiration from the immune system in order to build novel computational tools to solve problems in a vast range of domain areas. The basic immune theories used to explain how the immune system perform pattern recognition are described and their corresponding computational models are presented. This is followed with a survey from the literature of AIS applied to pattern recognition. The chapter is concluded with a trade-off between AIS and artificial neural networks as pattern recognition paradigms.

Keywords: Artificial Immune Systems, Negative Selection, Clonal Selection, Immune Network.

1 Introduction The vertebrate immune system (IS) is one of the most intricate bodily systems and its complexity is sometimes compared to that of the brain. With the advances in the biology and molecular genetics, the comprehension of how the immune system behaves is increasing very rapidly. The knowledge about the IS functioning has unravelled several of its main operative mechanisms. These mechanisms have demonstrated to be very interesting not only from a biological standpoint, but also under a computational perspective. Similarly to the way the nervous system inspired the development of artificial neural networks (ANN), the immune system has now led to the emergence of artificial immune systems (AIS) as a novel computational intelligence paradigm.

Artificial immune systems can be defined as


or metaphorical computational systems developed using ideas, theories, and components, extracted from the immune system. Most AIS aim at solving complex computational or engineering problems, such as pattern recognition, elimination, and optimisation. This is a crucial distinction between AIS and theoretical immune system models. While the former is devoted primarily to computing, the latter is focused on the modelling of the IS in order to understand its behaviour, 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 organised as follows. Section 2 describes relevant immune theories for pattern recognition and introduces their computational 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 Systems All living organisms are capable of presenting some type of defence 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 computational capabilities, as will be discussed throughout this section.

The immune system of vertebrates is composed 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 compartments performing complementary roles. The main task of the immune system is to survey the organism in the search for malfunctioning cells from their own body (e.g., cancer and tumour cells), and foreign disease causing elements (e.g., viruses and bacteria). Every element that can be recognised 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 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 B-cells and T-cells. These two types of cells are rather similar, but differ with relation to how they recognise antigens and by their functional roles. B-cells are capable of recognising antigens free in solution (e.g., in the blood stream), while T-cells require antigens to be presented by other accessory cells.

Fig. 1(a) illustrates that antigens are covered with molecules, named epitopes. These allow them to be recognised by the receptor molecules on the surface of B-cells, called antibodies (Ab). In contrast, Fig. 1(b) shows how for an antigen to be recognised by a T-cell receptor, it has to be processed and presented by an accessory cell.

Antigen Accessory B-cell Receptors (Ab) cell Epitopes

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(a) (b) Figure 1: Pattern recognition in the immune system. (a) B-cell recognising an antigen (Ag) free in solution. (b) T-cell recognising an antigen presented by an accessory cell.

Antigenic recognition is the first pre-requisite 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 outcome 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 T-cells. After T-cells are generated, they migrate into the thymus where they mature. During this maturation, all T-cells that recognise self-antigens are excluded from the population of T-cells; a process termed negative selection. If a B-cell encounters a nonself antigen with a sufficient affinity, it proliferates and differentiates into memory and effector cells; a process named clonal selection. In contrast, if a B-cell recognises a self-antigen, 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 computational algorithms counterparts.

2.1 Negative Selection The thymus is responsible for the maturation of T-cells; 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 outcome, the T-cells containing receptors capable of recognising these self antigens presented in the thymus are eliminated from the repertoire of T-cells through a process named negative selection [34]. All T-cells that leave the thymus to circulate throughout the body are said to be tolerant to self, i.e., 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 complement set (nonself) of the patterns to be recognised (self). A negative selection algorithm [14] has been proposed in the literature with applications focused on the problem of anomaly detection, such as computer 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 self-set, i.e., the nonself elements. The negative selection algorithm

runs as follows (Fig 2(a)):

1. Generate random candidate elements (C) using the same representation adopted;

2. Compare (match) the elements in C with the elements in P. If a match occurs, i.e., if an element of P is recognised by an element of C, then discard this element of C; else store this element of C in the detector set M.

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 composed of the set P plus other new patterns, or it can be a completely novel set.

Detector set Self (M) set (P)

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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 recognised 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.

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Figure 3: Clonal selection, expansion (proliferation), affinity maturation, and maintenance of memory cells. The highest affinity cells are selected to proliferate. Their progenies (clones) suffer mutation with high rates and those whose receptors present high affinity with the antigen are maintained as memory cells.

Some authors [15] 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 [10] 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 multi-modal 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 vice-versa;

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 vice-versa.

5. Add these mutated individuals to the population M and re-select 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 become increasingly better at its task of recognising patterns (antigens). Thus, based upon an evolutionarylike behaviour, CLONALG learns to recognise patterns.

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Figure 4: Immune network theory. The recognition of antigen by an antibody (cell receptor) leads to network activation, while the recognition of an idiotope by another antibody results in network suppression. Antibody Ab2 is said to be the internal image of the antigen Ag, because Ab1 is capable of recognising the antigen and also Ab2.

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