Abstract: |
A meta-GA (GA within a GA) is used to investigate evolving the parameter settings of genetic operators for genetic and evolutionary algorithms (GEA) in the hope of creating a self-adaptive GEA. We report three findings. First, the meta-GA can adapt its genetic operators to different problems and thereby perform well on average across diverse problems. Second, the meta-GA can change its parameters during the course of a run - seemingly a good idea - but this behavior may actually decrease performance. Finally, the genetic operator configurations the meta-GA evolves are far from optimal. We conclude that, while meta-GAs show promise for automating some parameter configurations, they are not likely to replace manually configured genetic and evolutionary algorithms without innovative alteration. |
