An approaching genetic algorithm for automatic beam angle selection in IMRT planning

Abstract

A method named approaching genetic algorithm (AGA) is introduced to automatically select the beam angles for intensity-modulated radiotherapy (IMRT) planning. In AGA, the best individual of the current population is found at first, and the rest of the normal individuals approach the current best one according to some specially designed rules. In the course of approaching, some better individuals may be obtained. Then, the current best individual is updated to try to approach the real best one. The approaching and updating operations of AGA replace the selection, crossover and mutation operations of the genetic algorithm (GA) completely. Using the specially designed updating strategies, AGA can recover the varieties of the population to a certain extent and retain the powerful ability of evolution, compared to GA. The beam angles are selected using AGA, followed by a beam intensity map optimization using conjugate gradient (CG). A simulated case and a clinical case with nasopharynx cancer are employed to demonstrate the feasibility of AGA. For the case investigated, AGA was feasible for the beam angle optimization (BAO) problem in IMRT planning and converged faster than GA.