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During our earlier research, it was recognised that in order to be successfulwith an indirect genetic algorithm approach using a decoder, the decoder has tostrike a balance between being an optimiser in its own right and findingfeasible solutions. Previously this balance was achieved manually. Here weextend this by presenting an automated approach where the genetic algorithmitself, simultaneously to solving the problem, sets weights to balance thecomponents out. Subsequently we were able to solve a complex and non-linearscheduling problem better than with a standard direct genetic algorithmimplementation.

Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem