Investigations in meta-GAs: panaceas or pipe dreams?

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 selfadaptive 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. Categories and Subject Descriptors J.2 [Physical Sciences and Engineering] – engineering. General Terms Algorithms, Performance, Experimentation Keywords Genetic Algorithms, Meta-GA, Adaptive Par...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Jeff Clune, Sherri Goings, Bill Punch, Eric Goodman
• Keywords: Evolutionary Algorithms, GECCO 2005, Genetic Operator, Parameter