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\section{Model Evaluation and Improvements}  The model was evaluated by measuring the amount of generations. Based on this metric, the basic algorithm is efficient (i.e. not many generations are required to get a solution that is relatively close to optimal). However, the model is not very accurate. A significant tradeoff between efficiency (the number of generations required to reach the optimal solution) and accuracy (F1 score). This score) was observed.  To mitigate this problem, the  model could be improved by introducing sexual reproduction, as in a traditional reproduction into the  genetic algorithm. Sexual reproduction would result in an tend to find the  optimal solution appearing sooner more efficiency  because sexual reproduction results in more produces greater  genetic diversity than asexual reproduction.\cite{reed2003correlation}