Abstract
Grammar Induction (or Grammar Inference or Language Learning) is the process of learning of a grammar from training data of the positive and negative strings of the language. Genetic algorithms are amongst the techniques which provide successful result for the grammar induction. This paper presents a stochastic Mutation Operator based on an Adapted Genetic Algorithm which works with random mask, with uniform distribution of bits over the chromosome length. The model has been implemented, and the results obtained for the set of four context free languages are presented. The paper also compares the suggested operator with other three mutation operator. The suggested operator has shown fast convergence for the induction of grammar as compared to the other operators used.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
