Stochastic mutation approach for grammar induction using Genetic Algorithm

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.