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In some cases the problem that needs to be optimized consists of self patterns that changes through time To address these types of problems, the dynamic clonal selection algorithm (DCS) was introduced by Kim and Bentley [461] The dynamic clonal selection algorithm is based on the AIS proposed by Hofmeyr [372] The basic concept is to have three di erent populations of ALCs, categorized into immature, mature and memory ALC populations Kim and Bentley [461] explored the e ect of three parameters on the adaptability of the model to changing self These parameters were the tolerization period, the activation threshold and the life span The tolerization period is a threshold on the number of generations during which ALCs can become self-tolerant The activation threshold is used as a measure to determine if a mature ALC met the minimum number of antigen matches to be able to become a memory ALC The life span parameter indicates the maximum number of generations that a mature ALC is allowed to be in the system If the mature ALC s life span meets the pre-determined life span parameter value, the mature ALC is deleted from the system The experimental results with di erent parameter settings indicated hat an increase in the life span with a decrease in the activation threshold resulted in the model to have an increase in detecting true nonself patterns An increase in the tolerization period resulted in less self patterns being detected falsely than non-self patterns, but only if the self patterns were stable With a changing self the increase in tolerization period had no remarkable in uence in the false detection of self patterns as non-self patterns Although the DCS could incrementally learn the structure of self and non-self patterns, it lacked the ability to learn any changes in unseen self patterns The memory ALCs in the DCS algorithm had in nite lifespan This feature was omitted in the extended DCS by removing memory ALCs that were not self-tolerant to newly introduced self patterns [460] A further extension to the DCS was done by introducing hyper-mutation on the deleted memory ALCs [459] The deleted memory ALCs were mutated to seed the immature detector population, ie deleted memory ALCs form part of a gene library Since these deleted memory ALCs contain information (which was responsible for giving them memory status), applying mutation on these ALCs will retain and ne tune the system, ie reinforcing the algorithm with previously trained ALCs.

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where fF is the number of antibodies that are added to the free-antibody layer, amax is the maximum possible distance between a B-Cell and an antigen pattern in the data space (ie lowest possible a nity), fa (zp , uk ) is the a nity between antigen, zp , and B-Cell, uk , and is some positive constant If an antigen does not bind to any of the B-Cells, a new B-Cell, unew , is created with the same presentation as the unbinded antigen, zp The new B-Cell is added to the B-Cell layer resulting in a more diverse coverage of antigen data The new B-Cell, unew , also produces mutated free antibodies, which are added to the free-antibody layer he nal layer, M, consists only of memory cells and only responds to new memory cells The clone, uk , is presented as a new memory cell to the memory layer, M The memory cell with the lowest a nity to uk is selected as vmin If the a nity between k and vmin is lower than the predetermined memory threshold, aM , and the a nity u of uk is less than the a nity of vmin with the antigen, zp (that was responsible for the creation of the new memory cell, uk ), then vmin is replaced by the new memory cell, uk If the a nity between uk and vmin is higher than the predetermined memory threshold, aM , the new memory cell, uk , is added to the memory layer The multi-layered model improved the SSAIS model [626] (discussed in Section 194) in that the multi-layered model obtained better compression on data while forming stable clusters.

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