Evaluation of Cost of Plans in Multiple Dependent Queries Execution Using G.A. Techniques
International Journal of Engineering and Technology
Many educational institutions in India have already established online teaching and learning methodologies with different capabilities and approaches. After inspired from foreign universities, they have successfully adopted the learning online network with computer assisted personalized approaches. Usually, two kinds of large data sets are involved with the system, e.g. educational resources such as web pages, demonstrations, simulations, and individualized problems designed for use on homework
... for use on homework assignments, and information about users who create, modify, assess, or use these resources. Genetic Algorithms (GAs) may be implemented as an effective tool to use in pattern recognition. The important aspect of GAs in a learning context is their use in pattern recognition. There are two different approaches for application of GA in pattern recognition. First of all apply a GA directly as a classifier. In this case G.A. may be applied to find the decision boundary in N dimensional feature space. Then use a GA as an optimization tool for resetting the parameters in other classifiers. Most applications of GAs in pattern recognition optimize some parameters in the classification process. In many applications of GAs, feature selection has been used. GAs has also been applied to find an optimal set of feature weights which improve classification accuracy. In this paper, it is intended to use a GA to optimize a combination of classifiers. The objective is to predict the students' semester grades of a reputed Engineering college of India based on some acquired features. It is also intended to evaluate the size of each chromosome, e.g. student level at each query level as well as cost of query plans which may be associated with the student level. The objective is also aimed to evaluate the performance of the queries as well as query plans associated with the student database.