doi:10.3850/978-981-08-7304-2_0294

ABSTRACT

As we all are aware that “Evolutionary Algorithms” (EAs) are modern techniques used for searching for an optimum. One can establish communication via two medium first oral communications (Speech Processing) and second is written (Text Processing). Natural Language processing is the area where we deal with both the approach of communication. This paper focuses the second medium (text processing) of communication where we communicate using any language by writing some thing. Genetic Algorithms are developed as random search methods, which have not so sensitivity on primary data of the problems. They can be used in estimation of system parameter to get the best possible solution. Genetic and Parallel Genetic Algorithms have been discussed for grammar induction. Grammar Inference or Language Learning is the process of learning grammar from training data. This paper mainly discussed the various methods for learning context-free grammar (CFG) from the corpus of string and presents the approach of informant learning in the form of result for two standard grammar problems called as “Balanced Parenthesis Grammar and Palindrome Grammar”. This paper also presents an architectural approach for load balancing for global class of genetic algorithm (Semi Synchronous Master-Slaves). In this paper we have used global parallel genetic algorithm for inducing grammar in the form of CFG and to accelerate the system performance.

Keywords: Machine learning, Grammatical inference, Natural language processing, Genetic algorithms, Parallel genetic algorithms etc.

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