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4.6.4 An Iterative Approach in Generating
(Variably Sized) Negative Detectors
Dasgupta et al. (2004) used real-valued representation on [0, 1] n , but it introduced a
diff erent way to generate hyperspherical negative detectors. h is approach is similar
to NSMutation (Ayara et al., 2002) but in real-valued space. At the beginning, an
initial population of candidate detectors is generated at random. Such detectors
then mature through an iterative process. In each iteration, the radius of each
detector is calculated as r d
=
D
r s , where r s is the variability around a self-point
(see Figure 4.12a).
During an iterative process, detectors are moved away from self-input data and
the other existing detectors. During this process, the detectors are ranked accord-
ing to their coverage. h e larger detectors are considered better fi t and selected to go
to the next generation. h e smaller detectors are discarded and replaced with clones
of the better-fi t detectors. A clone of a detector is generated by moving the center of
the original detector by a fi xed distance to its proximity. In addition, new random
detectors are introduced to explore new areas of the nonself space. h e detector
generation process terminates when a set of mature detectors that can provide sig-
nifi cant coverage of nonself coverage are evolved.
Let d
=
( c , r d ) be a detector. During the generation stage, the center of a detec-
tor is moved according to the following equation:
dir
dir
new
c
c
C nearest
C new
D
r s
C
C
D
C old
C new
r s
Self-points
(a)
(b)
(c)
Figure 4.12 The computational steps used during the detector maturation
process are illustrated as follows: (a) shows a way to calculate and update
the radius of a detector; (b) if a candidate detector overlaps with an existing
detector (or self-points), then the candidate detector (i.e., its center c ) is moved
in the opposite direction to its nearest neighbor detector; (c) given a mature
detector, a clone is created at a distance equal to its radius, and the direction
where it is created is selected at random.
 
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