A Clone Selection Based Real-Valued Negative Selection Algorithm

Algorithm 3

The process of CB-RNSA.

Procedure. CB-RNSA

Input: self training set Train, expected coverage rate p₀

Output: detector set D, boundary self set Self_o, outlier self set Self_d

n₀: the sampling frequency of non-self space, n₀

i: the number of non-self samples

m: the number of non-self samples which are covered by detectors

: candidate detector set = = = , , ,  

Clusters: cluster set Clusters =

: the number of candidate detector level

Begin

Initialize self training set Train, i = 0, m = 0, , , n₀ = ;

Initialize outlier self set Self_d according to Procedure outlier selves discovery algorithm;

Initialize cluster set Clusters according to Procedure clusters discovery algorithm;

While does not reach the maximum number of levels for candidate detectors do

Consider centers of Clusters as antigens, randomly generate initial immune cell population in the qualified range;

While true do

Select immune cells;

Generate copies of immune cells;

Mutate according to affinities;

Compute distances between mutated individual and every self in the training set Train;

If is recognized by some self Then discard ;

Else

Find the closest self to d_new, and add it to boundary self set Self_o;

i ++;

Compute distances between and every detector in the detector set D;

If d_new is not identified by any detector Then put it into the candidate detector set CD;

Else m ++;

End if;

If the number of non-self samples reaches the sample times Then

Compute current coverage rate p;

If p reaches the expected coverage rate , break;

Else incorporate candidate detector set CD with D, reset i, m, CD;

End if;

End;

l ++;

Changes the limited range of candidate detectors;

End;

End.