- author = "Istvan Jonyer and Akiko Himes",
- title = "Improving Modularity in Genetic Programming Using Graph-Based Data Mining",
- booktitle = "Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference",
- year = "2006",
- editor = "Geoff C. J. Sutcliffe and Randy G. Goebel",
- pages = "556--561",
- address = "Melbourne Beach, Florida, USA",
- month = may # " 11-13",
- publisher = "American Association for Artificial Intelligence",
- keywords = "genetic algorithms, genetic programming, Machine Learning and Discovery",
-
URL = "
http://www.aaai.org/Papers/FLAIRS/2006/Flairs06-110.pdf",
- abstract = "We propose to improve the efficiency of genetic programming, a method to automatically evolve computer programs. We use graph-based data mining to identify common aspects of highly fit individuals and modularising them by creating functions out of the subprograms identified. Empirical evaluation on the lawn mower problem shows that our approach is successful in reducing the number of generations needed to find target programs. Even though the graph-based data mining system requires additional processing time, the number of individuals required in a generation can also be greatly reduced, resulting in an overall speed-up.",
-
notes = "cited by \cite{Spector:2011:GECCO}
http://www.cs.miami.edu/~geoff/Conferences/FLAIRS-19/Schedule.shtml
http://www.aaai.org/Press/Proceedings/flairs06.php",
