  During our earlier research, it was recognised that in order to be successful
with an indirect genetic algorithm approach using a decoder, the decoder has to
strike a balance between being an optimiser in its own right and finding
feasible solutions. Previously this balance was achieved manually. Here we
extend this by presenting an automated approach where the genetic algorithm
itself, simultaneously to solving the problem, sets weights to balance the
components out. Subsequently we were able to solve a complex and non-linear
scheduling problem better than with a standard direct genetic algorithm
implementation.
