Kooperativer Bibliotheksverbund

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Format: 1 online resource (230 pages)

ISBN: 0-12-822154-2

Note: Front Cover -- STATE OF THE ART ON GRAMMATICAL INFERENCE USING EVOLUTIONARY METHOD -- STATE OF THE ART ON GRAMMATICAL INFERENCE USING EVOLUTIONARY METHOD -- Copyright -- Dedication -- Contents -- Foreword -- Preface -- Acknowledgment -- Abbreviations -- 1 - Introduction and scientific goals -- 1.1 Introduction -- 1.2 Why grammatical inference is popular -- 1.3 Scientific goals: why this book? -- 2 - State of the art: grammatical inference -- 2.1 Introduction -- 2.2 Part 1. Preliminary definitions -- 2.2.1 Backus-Naur form -- 2.2.2 Grammars -- 2.2.3 Chomsky hierarchy of grammars -- 2.2.4 Major grammar definitions -- 2.2.4.1 Unrestricted grammars -- 2.2.4.2 Context-sensitive grammars -- 2.2.4.3 Context-free grammars -- 2.2.4.4 Regular grammars -- 2.2.4.5 Regular expression -- 2.2.4.6 Matrix grammars -- 2.2.4.7 Programmed grammars -- 2.2.4.8 Random context-free grammars -- 2.2.4.9 Valence grammars -- 2.2.4.10 Bag context grammars -- 2.3 Part 2. Introduction to learning algorithms -- 2.3.1 Identification of the limit -- 2.3.1.1 Strengths and weaknesses -- 2.3.2 Teacher and queries learning algorithm -- 2.3.2.1 Strengths and weaknesses -- 2.3.3 Probably Approximately Correct learning algorithm -- 2.3.3.1 Strengths and weaknesses -- 2.3.4 Neural network in learning algorithm -- 2.3.4.1 Strengths and weaknesses -- 2.3.5 Automatic DIstillation of structure algorithm -- 2.3.5.1 Strengths and weaknesses -- 2.3.6 EMILE -- 2.3.6.1 Strengths and weaknesses -- 2.3.7 e-Grammar Induction Drive by Simplicity -- 2.3.7.1 Strengths and weaknesses -- 2.3.8 Computation learning of natural language -- 2.3.8.1 Strengths and weaknesses -- 2.3.9 Context Distribution Clustering algorithm -- 2.3.9.1 Strengths and weaknesses -- 2.3.10 Language agent method -- 2.3.10.1 Strengths and weaknesses -- 2.3.11 Genetic algorithm-based learning approach. , 2.3.11.1 Strengths and weaknesses -- 2.3.12 Architecture for learning linguistic structure -- 2.3.12.1 Strengths and weaknesses -- 2.3.13 Alignment-based learning -- 2.3.13.1 Strengths and weaknesses -- 2.3.14 Improved tabular representation algorithm -- 2.3.14.1 Strengths and weaknesses -- 2.4 Comparison and discussion -- 2.5 What are the challenges with grammatical inference algorithms? -- 2.6 Summary -- References -- 3 - State of the art: genetic algorithms and premature convergence -- 3.1 Introduction -- 3.2 Factors affecting genetic algorithms -- 3.3 Theoretical framework -- 3.3.1 Schema theory -- 3.3.2 Markov chain theory -- 3.3.3 Statistical mechanics -- 3.4 Approaches to preventing premature convergence -- 3.4.1 Crowding method -- 3.4.2 Strengths and weaknesses -- 3.4.3 Incest prevention algorithm -- 3.4.3.1 Strengths and weaknesses -- 3.4.4 Scheduled sharing approach -- 3.4.4.1 Strengths and weaknesses -- 3.4.5 Migration model and nCUBE-based approach -- 3.4.5.1 Strengths and weaknesses -- 3.4.6 Cooperation-based approach -- 3.4.6.1 Strengths and weaknesses -- 3.4.7 Syntactic analysis of convergence -- 3.4.7.1 Strengths and weaknesses -- 3.4.8 Pygmy algorithm -- 3.4.8.1 Strengths and weaknesses -- 3.4.9 Adaptive probability-based approach -- 3.4.9.1 Strengths and weaknesses -- 3.4.10 Social disaster technique -- 3.4.10.1 Strengths and weaknesses -- 3.4.11 Island model genetic algorithm -- 3.4.11.1 Strengths and weaknesses -- 3.4.12 Shifting balance theory in dynamic environment -- 3.4.12.1 Strengths and weaknesses -- 3.4.13 Random offspring generation approach -- 3.4.13.1 Strengths and weaknesses -- 3.4.14 Chaos operator-based approach -- 3.4.14.1 Strengths and weaknesses -- 3.4.15 Self-adaptive selection pressure steering approach -- 3.4.15.1 Strengths and weaknesses -- 3.4.16 Multicombinative strategy -- 3.4.16.1 Strengths and weaknesses. , 3.4.17 Genetic algorithm using self-organizing maps -- 3.4.17.1 Strengths and weaknesses -- 3.4.18 Age-layered population structure approach -- 3.4.18.1 Strengths and weaknesses -- 3.4.19 Number structuring approach -- 3.4.19.1 Strengths and weaknesses -- 3.4.20 Hybrid particle swarm optimization and genetic algorithm -- 3.4.20.1 Strengths and weaknesses -- 3.4.21 Selective mutation-based approach -- 3.4.21.1 Strengths and weaknesses -- 3.4.22 Elite mating pool genetic algorithm -- 3.4.22.1 Strengths and weaknesses -- 3.4.23 Dynamic application of reproduction operator -- 3.4.23.1 Strengths and weaknesses -- 3.4.24 Hybrid strategy using elite mating pool genetic algorithm and dynamic application reproduction operator -- 3.4.24.1 Strengths and weaknesses -- 3.4.25 Frequency crossover with nine different mutations -- 3.4.25.1 Strengths and weaknesses -- 3.5 Classifications and analyses -- 3.6 Challenges with the genetic algorithm -- 3.7 Summary -- References -- Further reading -- 4 - Genetic algorithms and grammatical inference -- 4.1 Introduction -- 4.2 Bit-mask oriented genetic algorithm -- 4.3 Bit-masking oriented data structure -- 4.4 Reproduction operators: crossover and mutation mask fill -- 4.4.1 Crossover operators -- 4.4.1.1 Cut crossover mask-fill operator -- 4.4.1.2 Bit-by-bit mask-fill crossover -- 4.4.1.3 Local cut crossover mask-fill operator -- 4.4.2 Mutation operator -- 4.4.2.1 Mutation mask-fill operator -- 4.5 New offspring generation -- 4.6 Genetic algorithm implemented for grammar induction -- 4.7 Maintaining regularity and generalization and minimum description length principle -- 4.8 Grammatical inference and minimum description length principle -- 4.9 Summary -- References -- Further reading -- 5 - Performance analysis of genetic algorithm for grammatical inference -- 5.1 Introduction -- 5.2 Simulation model and test languages. , 5.3 Parameter selection and tuning -- 5.3.1 Taguchi design for parameter quantification -- 5.4 Performance analysis of proposed bit masking-oriented genetic algorithm -- 5.4.1 Testing for premature convergence -- 5.4.1.1 Statistical test -- 5.4.2 Comparison with global optimization algorithms -- 5.4.2.1 Particle swarm optimization algorithm -- 5.4.2.2 Simulated annealing -- 5.4.2.3 Results and discussion -- 5.4.2.4 Statistical test -- 5.4.3 Comparison with other grammatical inference algorithms -- 5.4.3.1 The minimum description length principle in grammatical inference: an example -- 5.4.3.2 Results and discussions -- 5.4.3.3 Statistical test -- 5.5 Summary -- References -- 6 - Applications of grammatical inference methods and future development -- 6.1 Introduction -- 6.2 Application of grammatical inference method -- 6.3 Opportunities for future research -- 6.3.1 Handling noisy data -- 6.3.2 Dealing with natural languages -- 6.3.3 Developing a convergence model -- 6.3.4 Evolutionary algorithms for domain-specific languages -- 6.3.5 Categorizing the power of reproduction operators -- 6.3.6 Exploring opportunities in software testing -- References -- Subject index -- Author index -- Back Cover.

Additional Edition: Print version: Pandey, Hari Mohan State of the Art on Grammatical Inference Using Evolutionary Method San Diego : Elsevier Science & Technology,c2021 ISBN 9780128221167

Language: English