We employ a genetic algorithm approach to solving the persistent visitation problem for UAVs. The objective is to minimize the maximum weighted revisit time over all the sites in a cyclicly repeating walk. In general, the optimal length of the walk is not known, so this method (like the exact methods) assume some fixed length. Exact methods for solving the problem have recently been put forth, however, in the absence of additional heuristics, the exact method scales poorly for problems with more than 10 sites or so. By using a genetic algorithm, performance and computation time can be traded off depending on the application. The main contributions are a novel chromosome encoding scheme and genetic operators for cyclic walks which may visit sites more than once. Examples show that the performance is comparable to exact methods with better scalability.

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