A Proposed Genetic Algorithm Adaptation Based Model for Students’ Tracks Prediction

Abstract

Evolutionary algorithms such as genetic algorithms have proved their effectiveness and reliability in optimization solutions. The genetic algorithm is one of the most powerful algorithms in optimizing solutions to various problems. However, such algorithms suffer from performance issues resulting from bottlenecks in their mechanisms. This research proposes an effective solution for raising the performance of a genetic algorithm with the idea of merging its mechanism with one of the swarm intelligence techniques. The proposed solution presents an effective model for the initialization task as well as minimizing the iterations while ensuring the optimized solution. The mimic concept for natural processes has leveraged the genetic algorithm computation to the optimized level. Linking genetic algorithms and particle swarm intelligence algorithm has proved their effectiveness through a set of experiments. Moreover, the proposed adapted algorithm has been applied to two experiments to prove the effectiveness compared with literature and in the education field in generating the most effective track for students targeting to enhance the student’s performance which is considered one of the strategic targets in all economies