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 . 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. © Springer-Verlag Berlin Heidelberg 2003.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
González, F., Dasgupta, D., & Niño, L. (2003). A randomized real-valued negative selection algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2787, 261-272. https://doi.org/10.1007/978-3-540-45192-1_25