  One of the grand challenges in intensity-modulated radiotherapy (IMRT)
planning is to optimize the beam angles in a reasonable computation time. To
demonstrate the value of prior knowledge on improving the efficiency of beam
angle optimization (BAO) in IMRT, this study proposes a modified genetic
algorithm (GA) by incorporating prior knowledge into the evolutionary
procedure. In the so-called nowGABAO (kNOWledge guided GA for BAO) approach,
two types of prior knowledge are incorporated into the general flowchart of GA:
(1) beam angle constraints, which define the angle scopes through which no beam
is allowed to pass, and (2) plan templates, which define the most possible beam
configurations suitable to the studied case and are used to guide the
evolutionary progress of GA. Optimization tasks with different prior knowledge
were tested, and the results on two complicated clinical cases at the
head-and-neck and lung regions show that suitable defining of angle constraints
and plan templates may obviously speedup the optimization. Furthermore, a
moderate number of quite bad templates produce negligible influence on the
degradation of optimization efficiency. In conclusion, prior knowledge helps
improve the beam angle optimization efficiency, and can be suitably
incorporated by a GA. It is resistant to the inclusion of bad templates.
