Improving on Symbolic Learning System Based on Genetic Algorithm
Limei Feng1, Xizhao Wang
1Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University
Available Online October 2007.
- 10.2991/iske.2007.183How to use a DOI?
- GAssist system; Population initialization method; Fitness scaling; Hierarchical selection operator; MDL; New stop criterion
This paper uses GAssist system to get symbolic rules and proposes four techniques to improve it. A new population initialization method is applied and fitness scaling is used to promote the population’s convergence. It also improves on the deserted hierarchical selection operator and combines it with the MDL-based fitness function to control bloat effect. Finally, a new stop criterion to GA is studied. The experimental results show that the system is further improved. Comparing with other systems GA system is roughly comparable in generalization capacity but the efficiency needs improve
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Limei Feng AU - Xizhao Wang PY - 2007/10 DA - 2007/10 TI - Improving on Symbolic Learning System Based on Genetic Algorithm BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 1077 EP - 1083 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.183 DO - 10.2991/iske.2007.183 ID - Feng2007/10 ER -