State of the Art on Grammatical Inference Using Evolutionary Method (eBook)

State of the Art on Grammatical Inference Using Evolutionary Method presents an approach for  grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on evidence about the language. It has been extensively studied due to its high importance in various fields of engineering and science.  The book's prime purpose is to enhance the current state-of-the-art of grammatical inference methods and present new evolutionary algorithms-based approaches for context free grammar induction. The book's focus lies in the development of robust genetic algorithms for context free grammar induction.

The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for grammatical inference.

• Discusses and summarizes the latest developments in Grammatical Inference, with a focus on Evolutionary Methods
• Provides an understanding of premature convergence as well as genetic algorithms
• Presents a performance analysis of genetic algorithms as well as a complete look into the wide range of applications of Grammatical Inference methods
• Demonstrates how to develop a robust experimental environment to conduct experiments using evolutionary methods and algorithms

Dr. Hari Mohan Pandey is Lecturer in Computer Science at Edge Hill University, UK. He is specialized in Computer Science & Engineering. His research area includes artificial intelligence, soft computing techniques, natural language processing, language acquisition and machine learning algorithms. He is author of various books in computer science engineering (algorithms, programming and evolutionary algorithms). He has published over 50 scientific papers in reputed journals and conferences, served as session chair, leading guest editor and delivered keynotes. He has been given the prestigious award 'The Global Award for the Best Computer Science Faculty of the Year 2015” award for completing INDO-US project 'GENTLE”, award (Certificate of Exceptionalism) from the Prime Minister of India and award for developing innovative teaching and learning models for higher-education. Previously, he worked as a research fellow in machine learning at Middlesex University, London where he worked on a European Commission project- DREAM4CAR. His role was to research and develop advanced machine learning techniques relevant to the project goals and to evaluate these on both project and reference data sets, to lead and manage relevant work packages in support of the Project, ensuring appropriate interfacing with partners.

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State of the Art on Grammatical Inference Using Evolutionary Method presents an approach for grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on evidence about the language. It has been extensively studied due to its high importance in various fields of engineering and science. The book's prime purpose is to enhance the current state-of-the-art of grammatical inference methods and present new evolutionary algorithms-based approaches for context free grammar induction. The book's focus lies in the development of robust genetic algorithms for context free grammar induction. The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for grammatical inference. Discusses and summarizes the latest developments in Grammatical Inference, with a focus on Evolutionary Methods Provides an understanding of premature convergence as well as genetic algorithms Presents a performance analysis of genetic algorithms as well as a complete look into the wide range of applications of Grammatical Inference methods Demonstrates how to develop a robust experimental environment to conduct experiments using evolutionary methods and algorithms