A Voting-Based Agent System for Course Selection in E-Learning
In this work, alternative voting methods are compared to determine NASCAR rankings for the Sprint Cup Series. All of these methods make use only of the final placement of each driver in each race. We then construct a set of metrics to determine the effectiveness of each of these voting methods when compared to one another and the actual NASCAR scoring system. Finally, we attempt to generate a more optimal method, as defined by those same metrics, using a real-coded genetic algorithm. Our results show that most of the alternative voting methods vastly outperform the actual NASCAR system. Likewise, the method produced by the genetic algorithm outperforms even the best of the alternative methods.
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