An Enhanced Accuracy of a Prediction Model Having a Modified Genetic Algorithm with Cross-Average Crossover

Prediction models helped an organization in decision-making activities. However, enhancing an accuracy of a prediction model is an ongoing subject of research in the field of information technology. The study introduced a new prediction model having a modified genetic algorithm (GA) with Cross Average Crossover operator and rank based selection function to the existing model having k-means segmentation combined with C4.5 algorithms. Comparison of the accuracy of the existing model and the new model is presented in the simulation using the 4,410 records of student leaver's in a university. Simulation results showed that the new prediction model having GA with CAX and rank-based se- lection outperformed the model with generic GA with spliced crossover and roulette wheel selection method with respect to its accuracy.

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