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Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language provides the first comprehensive introduction to Grammatical Evolution, a novel 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 unique 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.
About the author
Michael O'Neill [BSc. (UCD), PhD (UL)] is a lecturer in the Department of Computer Science and Information Systems at the University of Limerick. He has over 70 publications on biologically inspired algorithms (BIAs). He coauthored the Springer title "Grammatical Evolution -- Evolutionary Automatic Programming in an Arbitrary Language", Genetic Programming Series, 2003, 160 pp., ISBN 1-4020-7444-1. He is one of the two original developers of the Grammatical Evolution algorithm, research that spawned an annual invited tutorial at the largest evolutionary computation conference and an international workshop, and is also on a number of relevant organising committees (e.g., GECCO 2005). Michael is a regular reviewer for the leading evolutionary computation (EC) journals, namely IEEE Trans. on Evolutionary Computation, MIT Press's Evolutionary Computation, and Springer's Genetic Programming and Evolvable Hardware journal.
Summary
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
provides the first comprehensive introduction to Grammatical Evolution, a novel 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 unique 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.
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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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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)
