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Genetic Congressional RedistrictingThis project serves to optimize United States congressional district maps in order to achieve improved outcomes in representation metrics and, hopefully, congressional productivity (i.e. more and better laws passed). The program achieves this by employing a genetic machine learning algorithm that gradually optimizes a population of district maps through random heuristic mutation and fitness selection, evaluating the maps on several important metrics, including population balance, compactness, win margin, and efficiency gap.
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