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16. Complete the following statements:
a. h e ————— the set points a detector covers, the more —————
the detector is.
b. In a nity maturation, the ————— the a nity of a B cell for patho-
gens present, the —————— likely it is that the B cell will clone.
c. Any type of variation operator can be applied as long as it guarantees a
good ——————— of the ——————— .
17. List the three main features that characterize a particular immune algorithm.
18. Why may the choice of a matching rule depend on a particular type of data?
Could you give an example where a specifi c matching rule does not work on
a particular data?
19. Give a formal representation where the best string-matching rule could be
a. Edit distance
b. Hamming distance
c. Binary distance
d. Value diff erence metric
e. Landscape-a nity matching
f. rcb matching
g. R-chunk matching rule
20. Defi ne and solve a problem by using immune models. Write the necessary
information to specify your problem with respect to the elements in the boxes
in Figure 3.5.
References
Aickelin, U. and S. Cayzer. h e danger theory and its application to artifi cial immune
systems. Proceedings of 1st International Conference on Artifi cial Immune Systems
(ICARIS) . University of Kent at Canterbury, U.K., September 9-11, 2002.
Balthrop, J., F. Esponda, S. Forrest and M. Glickman. Coverage and generalization in an
artifi cial immune system. Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO) , Morgan Kaufmann, New York, pp. 3-10, 2002.
Celada, F. and P. E. Seiden. A nity maturation and hypermutation in a simulation of the
humoral immune response. Eur. J. Immunol. , 26, 1350-1358, 1996.
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