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.