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Sinopsis
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
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Acerca del autor
Hari Mohan Pandey is a professor of data science and artificial intelligence at the School of Technology at Bournemouth University, UK. I am featured in the 2021, 2022, 2023, and 2024 World Ranking list of Top 2% scientists by Sandford University. I am specialized in Computer Science & Engineering. My research area includes artificial intelligence, soft computing techniques, natural language processing, language acquisition, machine learning, deep learning, and computer vision. I am the author of various books in computer science engineering (algorithms, programming, and evolutionary algorithms). Recently, my book entitled “State of the Art on Grammatical Inference Using Evolutionary Method " has been published in Elsevier. I have published over 150 scientific papers in reputed journals and conferences. I am serving on the editorial board of reputed journals (including Neural Networks Elsevier, Applied Soft Computing Elsevier, Swarm and Evolutionary Computing Elsevier, Neural Computing and Applications Springer, IEEE Transactions of Evolutionary Computation, IEEE Transactions on Industrial Informatics, Neurocomputing Springer, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning Systems and Knowledge-Based Systems Elsevier) as action editor, associate editor, and guest editor. I am the reviewer of top international conferences such as GECCO, CEC, IJCNN, BMVC, AAAI, etc. I have delivered expert talks as a keynote and invited speaker. I am a fellow of the HEA of the UK Professional Standards Framework (UKPSF) and have a rich teaching experience at the higher education level. I have delivered lecturers in international summer/winter schools. I have been given the prestigious award “The Global Award for the Best Computer Science Faculty of the Year 2015”, the award for completing the INDO-US project “GENTLE”, award (Certificate of Exceptionalism) from the Prime Minister of India, and the award for developing innovative teaching and learning models for higher education. In the past, I worked as a Sr. Lecturer in the Computer Science department at Edge Hill University. I also worked as a research fellow in machine learning at the School of Technology at Middlesex University London where I worked on a European Commission project- DREAM4CARS.
De la contraportada
<p>Multilingual text data are increasing day by day, which creates opportunities and challenges in the field of computational linguistics.<i> State of the Art on Grammatical Inference Using Evolutionary Method</i> enriches the domains of language processing with improved performance. This book is a resource that presents an approach for the grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on the evidence about the language. It has been extensively studied due to its high importance in the various fields of engineering and science. Typically, GI as a research domain uses the unsupervised learning procedure for extracting grammatical description from a set of corpus or textual data. Several proposed approaches to GI have faced various challenges; as such, researchers have been motivated to develop a more effective approach for grammatical inference.</p> <p>The prime purpose of this book is to enhance the current state of the art of grammatical inference method and present new evolutionary algorithms-based approaches for context free grammar induction. The focus is on the development of robust genetic algorithms for context free grammar induction. The key issue with any evolutionary method is handling premature convergence – a situation when diversity of population gets decreased over generations. 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 the grammatical inference. </p>
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