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Evolutionary algorithms are wildly used in unmanned aerial vehicle pathplanning for their flexibility and effectiveness. Nevertheless, they are sosensitive to the change of environment that can't adapt to all scenarios. Dueto this drawback, the previously successful planner frequently fail in a newscene. In this paper, a first-of-its-kind machine learning method namedEvolutionary Programmer is proposed to solve this problem. Concretely, the mostcommonly used Evolutionary Algorithms are decomposed into a series ofoperators, which constitute the operator library of the system. The new methodrecompose the operators to a integrated planner, thus, the most suitableoperators can be selected for adapting to the changing circumstances. Differentfrom normal machine programmers, this method focuses on a specific task withhigh-level integrated instructions and thus alleviate the problem of hugesearch space caused by the briefness of instructions. On this basis, a 64-bitsequence is presented to represent path planner and then evolved with themodified Genetic Algorithm. Finally, the most suitable planner is created byutilizing the information of the previous planner and various randomlygenerated ones.

Evolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms