Genetic Fuzzimetric Technique (GFT): A new optimization methodology using the concept of Fuzzimetric Arcs

Integration of fuzzy systems with genetic algorithm has been identified by researchers as a useful technique of optimizing systems under uncertainty. This integration is usually referred to as Genetic Fuzzy systems (GFS) where different researchers adopted different techniques to achieve the functionality of GFS. This paper proposes another new methodology based on the concept of Fuzzimetric Arcs which is also reviewed in this paper. This new proposed technique is termed as Genetic Fuzzimetric Technique (GFT) where the strength of this technique is based on the systematic approach of defining fuzzy sets (variables), cross-over and mutation of these variables in order to find the optimized performance. Most of real life decision making processes are of that type of uncertainty and hence the need of a fuzzy system that can be optimized. One such problem is to decide on the expected performance level of the student during the admission process to the university. This example was taken as a vehicle to clarify the mechanism of GFT.