A Randomized Real-Valued Negative Selection Algorithm

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.3

• Type: Conference
• Year: 2003
• Where: ICARIS
• Authors: Fabio A. González, Dipankar Dasgupta, Luis Fernando Niño
• Keywords: Algorithm, ICARIS 2003, Negative Selection Algorithm, Optimization Algorithm