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self-string. If a match was found, then such a candidate was rejected, otherwise,
it was accepted. h is process was repeated until a desired number of detectors were
generated. A probabilistic analysis was used to estimate the number of detectors
required to provide a desired level of reliability. h is straightforward approach is
given in the following algorithmic form (see Algorithm 1; note that although self is
considered as a set, for most applications it is a collection of data/patterns rather).
NS Algorithm 1: Exhaustive approach in
(binary) negative detector generation
=
=
Input: l , r , T
N where 1
r
l and S
U; l
string length, r
=
matching threshold, T
repertoire size
Output: Set D
U detectors generated using r -contiguous bits ( rcb )
matching rule
1 begin
=
2
D :
φ
<
3
while | D |
T do
4
Generate randomly bit string d
U
5
if d does not match any string in S then
=
6
D :
D
{ d }
7 end
h e major limitation of such random generation approach appears to be computa-
tional di culty of generating valid detectors, which grows exponentially with the
size of self. Also for many choices of l (“length”) and r (“recognition threshold”)
and compositions of self, random generation of strings for detectors may be pro-
hibitive. h us, this random approach is not e cient. Subsequently, a number of
detector generation approaches were proposed; these are presented in detail in the
following text.
4.3.2 A Dynamic Programming Approach
(Linear-Time Algorithm)
Given a collection of (equal-sized) self-strings S and a matching rule with partial
“matching threshold r , called the “ r -contiguous bits” ( rcb , described in Chapter 3).
h is algorithm works in two phases (D'haeseleer et al., 1996) as follows:
To count recurrences to defi ne an enumeration of all unmatched strings
(not r -matched) by S in the string space for the given self-set
To generate a detector set (according to the repertoire size) by the counting
recurrence, which is used to pick the detectors at random from the set of
candidate unmatched strings
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