González
Fabio
Niño
Luis Fernando
Hart
Emma
Dasgupta
Dipankar
University of Memphis
Division of Computer Science, The University of Memphis, Memphis, TN 38152, USA
Numerical and Computational Mathematics
2019-04-15T14:58
261-272
https://scigraph.springernature.com/explorer/license/
chapters
2003-01-01
This paper presents a real-valued negative selection algorithm with good mathematical foundation that solves some of the drawbacks of our previous approach [11]. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. The proposed method is a randomized algorithm based on Monte Carlo methods. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.
true
http://link.springer.com/10.1007/978-3-540-45192-1_25
2003
en
A Randomized Real-Valued Negative Selection Algorithm
chapter
Berlin, Heidelberg
Springer Berlin Heidelberg
Peter J.
Bentley
978-3-540-40766-9
978-3-540-45192-1
Artificial Immune Systems
Depto. de Ing. de Sistemas, Universidad Nacional de Colombia, Bogotá, Colombia
National University of Colombia
doi
10.1007/978-3-540-45192-1_25
dimensions_id
pub.1035052657
readcube_id
a6fa73ec53d8b3a1f10b7b9030ac8f0748c1955fdf0a576b9aba63b7c232efb2
Springer Nature - SN SciGraph project
Mathematical Sciences
Jon
Timmis