Название: State of the Art on Grammatical Inference Using Evolutionary MethodАвтор: Hari Mohan Pandey
Издательство: Academic Press/Elsevier
Год: 2022
Страниц: 230
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
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.
Grammatical inference, that is, learning a formal grammar from a set of observations, has many practical applications. These arise in natural language processing (NLP), robotics and control systems, structural pattern recognition, computational linguistics, automatic translation, computational biology, inductive logic programming, document management, compression, applications to time series, data mining, and many other areas. As with many challenges in Machine Learning, grammatical inference remains “unsolved.” Yet, over the years, a variety of machine approaches have been applied to these problems, with varying degrees of success depending on the application and the implementation. In this book, Prof. Pandey provides a very helpful overview of some of these methods, and also offers methods and results based on his own approaches within the field of evolutionary computation. Mainly, the techniques focus on derivatives of canonical genetic algorithms and swarm optimization, but the reader will be able to take any of Prof. Pandey’s efforts and investigate and extend them for their own purposes using any other approach that is worthy of exploration.
Key Features:
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
Readership:
Graduates, PhD students and lecturers in computer science, engineering and natural sciences as well as scientific researchers and biomedical engineers
Table of Contents:
1. Introduction and Scientific Goals
2. State of the Art: Grammatical Inference
3. State of the Art: Genetic Algorithms and Premature Convergence
4. Genetic Algorithms and Grammatical Inference
5. Performance Analysis of Genetic Algorithm for Grammatical Inference
6. Applications of Grammatical Inference Methods and Future Development
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