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A Genetic Fuzzy System (GFS) is basically a fuzzy system augmented by a learning process based on a genetic algorithm (GA). Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains.

Fault diagnosis in electronic circuits is an emerging area of research, where fully automated diagnosis systems are being developed for the investigation of the circuits. Developing test methods for the diagnosis of faults in analog circuits is still a complex task.

In recent decades, depletion of fossil fuels and its demand emerge as renewable energy in the field of power generation. Amid eco-friendly based renewable energy, wind and photovoltaic play a vital role in power generation.

Clustering finds various applications in the field of medical and telecommunication for unsupervised learning which is much required in expert system and its application. Various algorithms have been developed to clustering for the past fifty years.

The channels used to convey the human emotions consider actions, behaviours, poses, facial expressions, and speech. An immense research has been carried out to analyse the relationship between the facial emotions and these channels.

Nonlinear system identification process, especially bilinear system identification process exploits global optimization algorithms for betterment of identification precision. This paper attempts to introduce a new optimization algorithm.

Optimization algorithms are applied on Fuzzy system for various purposes like membership function optimization, co-efficient optimization, rule generation, rule selection, etc. Here we amalgamate Bat algorithm with fuzzy classifier to generate optimized rules.

In recent decades, region growing methods in image segmentation plays a vital role in medical image processing. Nonetheless, the method needs more advancement to cope up with the images of current acquisition devices. This paper attempts to solve the problem

In this paper, a systematic comparative analysis is presented on various static and adaptive mutation techniques to understand their nature on genetic algorithm. Three most popular random mutation techniques and a deterministic mutation technique.