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Books : reviews

Michael O'Neill, Conor Ryan.
Grammatical Evolution: evolutionary automatic programming in an arbitrary language.
Kluwer. 2003

rating : 3.5 : worth reading
review : 18 October 2007

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 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 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.

Grammatical Evolution is a new kind of evolutionary algorithm that evolves computer programs in an indirect manner. The evolved “genotype” is a string of numbers (in contrast to the explicit program code genotype of Genetic Programming, for example). These numbers are decoded into a program by using them to index into a BNF description of the program’s grammar, and using the indexed productions as the components of the generated program.

This book, written by GE’s inventors, explains the process. There is some biological background, a description of the GE algorithm itself, and some simple case-studies demonstrating its effectiveness. If you want a brief, clear overview of GE, and some background material, here it is.

Ian Dempsey, Michael O'Neill, Anthony Brabazon.
Foundations in Grammatical Evolution for Dynamic Environments.
Springer. 2009

Dynamic environments abound, encompassing many real-world problems in fields as diverse as finance, engineering, biology and business. A vibrant research literature has emerged which takes inspiration from evolutionary processes to develop problem-solvers for these environments.

Foundations in Grammatical Evolution for Dynamic Environments is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments. The book provides a clear introduction to dynamic environments and the types of change that can occur in them. This is followed by a detailed description of evolutionary computation, concentrating on the powerful Grammatical Evolution methodology. The book continues by addressing fundamental issues facing all Evolutionary Algorithms in dynamic problems, such as how to adapt and generate constants, how to enhance evolvability and maintain diversity. Finally, the developed methods are illustrated with application to the real-world dynamic problem of trading on financial time-series.

Anthony Brabazon, Michael O'Neill, Sean McGarraghy.
Natural Computing Algorithms.
Springer. 2015

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The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design.

This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.