A study on fuzzy rules discovery using Pseudo-Bacterial Genetic Algorithm with adaptive operator

Differentiated Quality of Service in Application Layer Active Networks

“…Inman [21] pointed out that gene interchange (as observed in bacteria [22,23]) could provide the rapid learning required. This was recently demonstrated by Furuhashi [24] for a bounded, globally optimised GA. In previous work [25] we have demonstrated that a novel unbounded, distributed GA with "bacterial learning" is an effective adaptive control algorithm for the distribution of services in an active service provision system derived from the application layer active network (ALAN).…”

Automatic design of fuzzy logic controller using a genetic algorithm—to predict power requirement and surface finish in grinding

“…Thus, there is still a need for the development of an efficient technique for optimal fuzzy rule generation. Genetic algorithm (GA), a population-based search and optimisation technique [14], had also been used by Hwang and Thompson [15], Kropp and Baitinger [16], Thrift [17], Hashiyama et al [18], Eiji Nawa et al [19], Cupal and Wilamowski [20], Domanski and Arabas [21], Herrera et al [22], and others for this purpose. Moreover, Whitley et al [23] and Ishigami et al [24] proposed the combined GA-NN technique for fuzzy rule generation.…”

Pseudo-Bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation

“…Nawa, Hashiyama, Furuhashi and Uchikawa [22] proposed a novel kind of evolutionary algorithm called the pseudo-bacterial genetic algorithm, which was successfully applied to extract rules on a set of input and output data. This algorithm introduced a genetic operator called bacterial mutation that has demonstrated to be useful in environments with a weak relationship between the parameters of a system.…”