Artificial Immune Models in .NET

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19 Artificial Immune Models
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False positives
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aneg False negatives
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Unknown self
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Figure 192 Adapted Negative Selection algorithm to evolve ALCs towards the selected class of non-self patterns in the training set and further away from the selected class of self patterns Once the tness of the ALC set evolves to a point where all the non-self patterns and none of the self patterns are detected, the ALCs represent a description of the concept If the training set of self and non-self patterns is noisy, the ALC set will be evolved until most of the non-self patterns are detected and as few as possible self patterns are detected The evolved ALCs can discriminate between examples and counter-examples of a given concept Each class of patterns in the training set is selected in turn as self and all other classes as non-self to evolve the di erent concept in the training set Gonzalez et al [327] present a negative selection method that is able to train ALCs with continuous-valued self patterns The ALCs are evolved away from the training set of self patterns and well separated from one another to maximize the coverage of nonself, ie the least possible overlap among the evolved set of ALCs is desired A similar approach is presented in the model of Graa and Engelbrecht [332] All patterns are represented as binary strings and the Hamming distance is used as a nity measure A genetic algorithm is used to evolve ALCs away from the training set of self patterns towards a maximum non-self space coverage and a minimum overlap among existing ALCs in the set The di erence to the model of Gonzalez et al [327], is that each ALC in the set has an a nity threshold The ALCs are trained with an adapted negative selection method as illustrated in Figure 192 With the adapted negative selection method the a nity threshold, aneg , of an ALC is determined by the distance to the closest self pattern from the ALC The a nity threshold, aneg , is used to determine a match with a non-self pattern Thus, if the measured a nity between a pattern and an ALC is less than the ALC s a nity threshold, aneg , the pattern is classi ed as a non-self pattern Figure 192 also illustrates the drawback of false positives and false negatives when the ALCs are trained with the adapted negative selection method These drawbacks are due to an incomplete static self set The known self is the incomplete static self set that is used to train the ALCs
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193 Clonal Selection Theory Models
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and the unknown self is the self patterns that are not known during training The unknown self can also represent self patterns that are outliers to the set of known self patterns Surely all evolved ALCs will cover non-self space, but not all ALCs will detect non-self patterns Therefore, Graa and Engelbrecht [332] proposed a transition function, the life counter function, to determine an ALC s status ALCs with annihilated status are removed in an attempt only to have mature and memory ALCs with optimum classi cation of non-self patterns
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Clonal Selection Theory Models
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The process of clonal selection in the natural immune system was discussed in Section 185 Clonal selection in AIS is the selection of a set of ALCs with the highest calculated a nity with a non-self pattern The selected ALCs are then cloned and mutated in an attempt to have a higher binding a nity with the presented non-self pattern The mutated clones compete with the existing set of ALCs, based on the calculated a nity between the mutated clones and the non-self pattern, for survival to be exposed to the next non-self pattern This section discusses some of the AIS models inspired by the clonal selection theory and gives the pseudo code for one of these AIS models
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