Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language

by Michael O'Neill, Conor Ryan

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.
Beginning with an overview of the necessary background material in Genetic Programming and Molecular Biology, Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language outlines the current 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 Grammatical Evolution and presents possible directions for future research.

160 pages, Paperback

First published May 1, 2003

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Reviews

[Adil Raja · Rating 5 out of 5]

I read this book almost eleven years ago. This is a remarkable book and you must definitely read it if you are into evolutionary algorithms, machine learning, genetic programming, computer science and/or optimization. The authors introduce a novel way of automatically generating computer programs for solving user-specified problems. The authors begin by talking about the genetic analog from which they conceived the idea. Then they speak about how a genetic algorithm could be coupled with a grammar specified in a Backus-Naur Form to generate computer programs. In short, this is a small book that you can almost finish in a single sitting. And you will stand up well equipped with the technical and conceptual underpinnings of the algorithm.

[Xianshun Chen · Rating 4 out of 5]

The book I used to develop my own grammatical evolution library