Using Ontologies to Express Prior Knowledge for Genetic Programming
Ontologies are useful for modeling domains and can be used to capture expert knowledge about a system. Genetic programming can be used to identify statistical relationships or models from data. Combining expert knowledge as well as statistical rules identified solely from data is necessary in application domains where data is scarce and a large body of expert knowledge exists.
We therefore study if the performance of genetic programming can be improved by incorporating prior knowledge from an ontology. In particular, we include prior knowledge as additional features for genetic programming.
The approach is tested with six benchmark data sets where we compare the required computational effort that is necessary to find an acceptable model with and without additional features. The results show that additional features gathered from an ontology improve the performance of tree-based GP. The probability to find acceptable solutions with a fixed computational budget is increased. For noisy data sets we observed the same effect as for the data sets without noise.
KeywordsSupervised learning Ontologies Domain knowledge Genetic programming Symbolic regression
- 6.Cramer, N.L.: A representation for the adaptive generation of simple sequential programs. In: Proceedings of the First International Conference on Genetic Algorithms, pp. 183–187 (1985)Google Scholar
- 7.Cruz, I.F., Xiao, H.: The role of ontologies in data integration. Eng. Intell. Syst. Electr. Eng. Commun. 13(4), 245 (2005)Google Scholar
- 14.Hansen, N., Auger, A., Ros, R., Finck, S., Pošík, P.: Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1689–1696. ACM (2010)Google Scholar
- 17.Kommenda, M., Kronberger, G., Wagner, S., Winkler, S., Affenzeller, M.: On the architecture and implementation of tree-based genetic programming in HeuristicLab. In: Companion Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO 2012), pp. 101–108. ACM (2012)Google Scholar
- 26.Schoenauer, M., Sebag, M.: Using domain knowledge in evolutionary system identification. CoRR abs/cs/0602021 (2006). http://arxiv.org/abs/cs/0602021
- 28.Whigham, P.A., et al.: Grammatically-based genetic programming. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-world Applications, vol. 16, pp. 33–41 (1995)Google Scholar
- 30.Winkler, S.M.: Evolutionary system identification: modern concepts and practical applications. Ph.D. thesis, Johannes Kepler University, Altenbergerstr. 69, 4040 Linz (2008)Google Scholar