Grammatical Evolution

Evolutionary Automatic Programming in an Arbitrary Language

By: Michael O'Neill, Conor Ryan

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Hardcover | 31 May 2003

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Hardcover
164 Pages

Dimensions(cm)
19.05 x 15.88 x 1.27

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This title provides a comprehensive introduction to grammatical evolution, an approach to genetic programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search. Grammatical evolution's rich modularity gives a flexibility, making it possible to use alternative search strategies-whether evolutionary, deterministic or some other approach-and to even radically change its behavior by merely changing the grammar supplied. This approach to genetic programming represents a powerful new weapon in the machine learning toolkit that can be applied to a diverse set of problem domains. Beginning with an overview of the necessary background material, this work outlines the state of the art in grammatical and genotype-phenotype-based approaches. Following a description of grammatical evolution and its application to a number of example problems, an in-depth analysis of the approach is conducted, focusing on areas such as the degenerate genetic code, wrapping, and crossover. The book continues with a description of hot topics in the subject and presents possible directions for future research.

Industry Reviews

From the reviews: "This is the first book written on grammatical evolution, a new technique that is receiving increasing attention and use. Therefore, the book fulfills an important role ! . The book contains a good description of grammatical evolution ! . 'Grammatical Evolution' should be useful for specialists and Ph.D. students in the field of grammatical evolution and genetic programming, and people working in artificial intelligence and genetic algorithms in general. We would advise it as a good resource for university libraries." (Manuel Alfonseca and Alfonso Ortega, Genetic programming and Evolvable Machines, Vol. 5, 2004)

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Programming & Scripting Languages Computer Science Artificial Intelligence Machine Learning Language & Linguistics Linguistics Grammar, Syntax & Morphology Language Learning & Teaching

Language Teaching & Learning Material & Coursework Grammar, Vocabulary & Pronunciation

Preface	p. ix
Foreword	p. xi
Acknowledgments	p. xv
Introduction	p. 1
Evolutionary Automatic Programming	p. 1
Molecular Biology	p. 2
Grammars	p. 2
Outline	p. 3
Survey of Evolutionary Automatic Programming	p. 5
Introduction	p. 5
Evolutionary Automatic Programming	p. 6
Origin of the Species	p. 8
Tree-based Systems	p. 10
Genetic Programming	p. 11
Grammar based Genetic Programming	p. 13
Backus Naur Form	p. 13
Cellular Encoding	p. 16
Bias in GP	p. 16
Genetic Programming Kernel	p. 16
Combining GP and ILP	p. 16
Auto-parallelisation with GP	p. 17
String based GP	p. 17
BGP	p. 18
Machine Code Genetic Programming	p. 19
Genetic Algorithm for Deriving Software	p. 20
CFG/GP	p. 21
Conclusions	p. 21
Lessons from Molecular Biology	p. 23
Introduction	p. 23
Genetic Codes & Gene Expression Models	p. 24
Neutral Theory of Evolution	p. 26
Further Principles	p. 28
Desirable Features	p. 29
Conclusions	p. 32
Grammatical Evolution	p. 33
Introduction	p. 33
Background	p. 34
Grammatical Evolution	p. 35
The Biological Approach	p. 36
The Mapping Process	p. 36
Backus Naur Form	p. 37
Mapping Process Outline	p. 39
Example Individual	p. 40
Genetic Code Degeneracy	p. 42
The Search Algorithm	p. 44
Discussion	p. 45
Conclusions	p. 47
Four Examples of Grammatical Evolution	p. 49
Introduction	p. 49
Symbolic Regression	p. 49
Results	p. 51
Symbolic Integration	p. 52
Results	p. 52
Santa Fe Ant Trail	p. 55
Results	p. 56
Caching Algorithms	p. 57
Results	p. 60
Conclusions	p. 62
Analysis of Grammatical Evolution	p. 63
Introduction	p. 63
Wrapping Operator	p. 64
Results	p. 64
Invalid Individuals	p. 64
Cumulative Frequency of Success	p. 65
Genome Lengths	p. 65
Discussion	p. 67
Degenerate Genetic Code	p. 67
Results	p. 69
Diversity Measures	p. 70
Discussion	p. 72
Removal of Wrapping and Degeneracy	p. 72
Results	p. 72
Mutation Rates	p. 74
Results	p. 76
Conclusions	p. 77
Crossover in Grammatical Evolution	p. 79
Introduction	p. 79
Homologous Crossover	p. 81
Experimental Approach	p. 81
Results	p. 83
Discussion	p. 91
Headless Chicken	p. 92
Experimental Approach	p. 93
Results	p. 94
Discussion	p. 95
Conclusions	p. 98
Extensions & Applications	p. 99
Translation	p. 99
Alternative Search Strategies	p. 101
Grammar Defined Introns	p. 102
GAUGE	p. 103
Problems	p. 105
Onemax	p. 106
Results	p. 107
Mastermind - a deceptive ordering version	p. 108
Results	p. 109
Discussion	p. 112
Conclusions and Future Work	p. 112
Chorus	p. 113
Example Individual	p. 114
Results	p. 116
Financial Prediction	p. 117
Trading Market Indices	p. 117
Experimental Setup & Results	p. 119
Adaptive Logic Programming	p. 121
Logic Programming	p. 121
GE and Logic Programming	p. 123
Backtracking	p. 124
Initialisation	p. 125
Discussion	p. 125
Sensible Initialisation	p. 125
Genetic Programming	p. 127
Conclusions	p. 128
Conclusions & Future Work	p. 129
Summary	p. 129
Future Work	p. 130
Index	p. 143
Table of Contents provided by Ingram. All Rights Reserved.

Grammatical Evolution

Evolutionary Automatic Programming in an Arbitrary Language

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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