Adapting Operator Settings in Genetic Algorithms

In the majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has been argued that these settings should vary over the course of a genetic algorithm run—so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an investigation into this question. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems and a hard problem from operations research, the flowshop sequencing problem, are therefore examined. The results obtained indicate that the applicability of operator adaptation is dependent upon three basic assumptions being satisfied by the problem being tackled.