Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT)

Abstract

Decision making and/or Decision Support Systems (DSS) using intelligent techniques like Genetic Algorithm and fuzzy logic is becoming popular in many new applications. Combining these techniques provides an enhanced capability of any decision support systems (DSS. This paper discusses a modular approach toward implementing Genetic Fuzzy system termed as “Genetic Fuzzimetric Technique” (GFT). The technique utilizes input importance factor to combine the modular structure into final decision process. The objective of this combination provides the ability of the system to interact and “take decision” in an environment in the same manner as the human decision maker would do. This proposed system is ideal in cases where mathematical modeling either does not exist or insufficient for appropriate decision making under uncertainty. Most of real life decision making processes are of that type of uncertainty. One such problem is to decide on the predicted GPA level for students during the admission process to the university. This is mainly dependent on High School (HS) performance, Sophomore Exam (SE) results and English exam (EEE) performance. Looking at the historical data of students, fuzzy logic can be used to develop rules based on these data. Genetic Algorithm would be used to optimize the performance of the system.