Data mining of genetic programming run logs

Authors:
Vic Ciesielski;Xiang Li
Affiliations:
RMIT University, Melbourne, Vic, Australia;RMIT University, Melbourne, Vic, Australia
Venue:
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Year:
2007

Citing 5
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Abstract

We have applied a range of data mining techniques to a data base of log file records created from genetic programming runs on twelve different problems. We have looked for unexpected patterns, or golden nuggets in the data. Six were found. The main discoveries were a surprising amount of evaluation of duplicate programs across the twelve problems and one case of pathological behaviour which suggested a review of the genetic programming configuration. For problems with expensive fitness evaluation, the results suggest that there would be considerable speedup by caching evolved programs and fitness values. A data mining analysis performed routinely in a GP application could identify problems early and lead to more effective genetic programming applications.