Clonal Optimization of Negative Selection Algorithm with Applications in Motor Fault Detection

TL;DR: In this article, a fault detection method of a wheeled mobile robot is described in which the method comprises the following steps: step one, collecting data including right-wheel rotating speed, relative displacement, and relative deflection angle of an entity robot in a normal running state and in a movement mechanism abnormality state; step two, constructing an autologous space formed by a suprasphere by applying the collected normal data; step three, generating a non-autologous detector that is used for movement mechanisms abnormality detection and is needed by a negative selection algorithm,

TL;DR: This study shows that NSA’s evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains and presents alternative approaches to NSA for comparison and analysis.

TL;DR: GB-RNSA lowers the number of detectors, time complexity, and false alarm rate, and certain methods are adopted to reduce duplication coverage between detectors, which achieves fewer detectors covering the nonself space as much as possible.

TL;DR: This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response and derives two versions of the algorithm, derived primarily to perform machine learning and pattern recognition tasks.

TL;DR: A method for change detection which is based on the generation of T cells in the immune system is described, which reveals computational costs of the system and preliminary experiments illustrate how the method might be applied to the problem of computer viruses.

TL;DR: A comprehensive review of the theory of genetic programming can be found in this paper, where the authors provide a coherent consolidation of recent work on the theoretical foundations of GP and genetic algorithms.