A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
Learning grammars with a modified classifier system
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Playing a toy-grammar with GCS
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Grammar-based classifier system: a universal tool for grammatical inference
WSEAS Transactions on Computers
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This paper describes an evolutionary approach to the problem of inferring non-stochastic context-free grammar (CFG) from natural language (NL) corpora. The approach employs Grammar-based Classifier System (GCS). GCS is a new version of Learning Classifier Systems in which classifiers are represented by CFG in Chomsky Normal Form. GCS has been tested on the NL corpora, and it provided comparable results to the pure genetic induction approach, but in a significantly shorter time. The efficient implementation for grammar induction is very important during analysis of large text corpora.