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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
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.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
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.