  Evolutionary algorithms are wildly used in unmanned aerial vehicle path
planning for their flexibility and effectiveness. Nevertheless, they are so
sensitive to the change of environment that can't adapt to all scenarios. Due
to this drawback, the previously successful planner frequently fail in a new
scene. In this paper, a first-of-its-kind machine learning method named
Evolutionary Programmer is proposed to solve this problem. Concretely, the most
commonly used Evolutionary Algorithms are decomposed into a series of
operators, which constitute the operator library of the system. The new method
recompose the operators to a integrated planner, thus, the most suitable
operators can be selected for adapting to the changing circumstances. Different
from normal machine programmers, this method focuses on a specific task with
high-level integrated instructions and thus alleviate the problem of huge
search space caused by the briefness of instructions. On this basis, a 64-bit
sequence is presented to represent path planner and then evolved with the
modified Genetic Algorithm. Finally, the most suitable planner is created by
utilizing the information of the previous planner and various randomly
generated ones.
