A General Framework for Evolving Multi-Shaped Detectors in Negative Selection

This paper presents a framework to generate multi-shaped detectors for real-valued negative selection algorithms. The detectors can take the form of hyper-rectangles, hyper-spheres and hyper-ellipses in non-self space. The uniform representation strategy and the evolutionary mechanism proposed in this work can serve as a baseline for further extension to use several shapes, generally providing an efficient coverage of detector space. In particular, these novel pattern detectors (in the complement space) are evolved using a genetic search (the structured genetic algorithm), which uses hierarchical genomic structures and a gene activation mechanism to encode multiple shapes. This genetic search helps in maintaining diverse shapes while contributing to the proliferation of best suited detector shapes in expressed genotype. The results showed that a significant coverage of the non-self space can be achieved using fewer detectors than other approaches using only a single type of detectors.