Performance Comparison between Genetic Algorithm and Population-Based Incremental Learning Algorithm using Student Ranking Application

Abstract

This paper compares the performance of Genetic Algorithm and Population-based Incremental Learning algorithm on a student ranking application. The proposed student ranking application focuses on proving the students' achievements in fields like attendance, achievements and backlogs apart from academics. This application produces the output according to the prioritized weights inputted by the user. Genetic Algorithm and Population-based Incremental Learning is evolutionary search based optimization algorithm which can arrive at near-optimal solution faster by using randomized techniques. These are stochastic search based algorithms that include randomness to escape local optima. The two algorithms are compared based on the accuracy of optimal value, number of generations and time taken to reach optimal value.