Improving on Symbolic Learning System Based on Genetic Algorithm

DOI

10.2991/iske.2007.183How to use a DOI?

Keywords

GAssist system; Population initialization method; Fitness scaling; Hierarchical selection operator; MDL; New stop criterion

Abstract

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

Copyright

© 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/).

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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  -