Investigations in meta-GAs: panaceas or pipe dreams?

Clune J
Goings S
Goodman ED
Punch W

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.

Pub. Info: 
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). 235-241. Washington, D.C.

author = {Clune, Jeff and Goings, Sheni and Punch, Bill and Goodman, Eric},
title = {Investigations in meta-GAs: Panaceas or Pipe Dreams?},
booktitle = {Proceedings of the 7th Annual Workshop on Genetic and Evolutionary Computation},
series = {GECCO '05},
year = {2005},
location = {Washington, D.C.},
pages = {235--241},
numpages = {7},
url = {},
doi = {10.1145/1102256.1102311},
acmid = {1102311},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {adaptive parameter control, genetic algorithms, meta-GA},