Created by W.Langdon from gp-bibliography.bib Revision:1.4941
Since man began to dream of machines that could automate not only the more mundane and laborious tasks of everyday life, but that could also improve some of the more agreeable aspects, he has turned to nature for inspiration. This inspiration has taken all sorts of forms, with inventors producing everything from Icarus-like, bird-inspired flying machines to robots based on some of the more mechanically useful human appendages.
Another inspiration that can be taken from nature is to employ its tools, rather than necessarily employing its products. In this way, the field of evolutionary computation has taken stock of the power of evolution, and applied it, albeit at a very coarse level, to problem solving. Genetic Programming, a powerful incarnation of evolutionary computation uses the artificial evolutionary process to automatically generate programs. The adoption of evolution to automatic generation of programs represents one of the most promising approaches to that holy grail of computer science, automatic programming, that is, a computer that can automatically generate a program from scratch given a high-level problem description.
Research in Genetic Programming has explored a number of program representations beyond the original Lisp S-expression syntax trees, and some of the more powerful of these incorporate a developmental strategy that transforms an embryonic state into a fully fledged adult program.
The form of Genetic Programming presented in this book, Grammatical Evolution, delves further into nature's processes at a molecular level, embracing the developmental approach, and drawing upon a number of principles that allow an abstract representation of a program to be evolved.
This abstraction enables firstly, a separation of the search and solution spaces that allow the EA search engine to be a plug-in component of the system, facilitating the exploitation of advances in EAs by GE. Secondly, this allows the evolution of programs in an arbitrary language with the representation of a program's syntax in the form of a grammar definition.
Thirdly, the existence of a degenerate genetic code is enabled, giving a many-to-one mapping, that allows the exploitation of neutral evolution to enhance the search efficiency of the EA. Fourthly, we can adopt the use of a wrapping operator that allows the reuse of genetic material during a genotype-phenotype mapping process.
This book is partly based on the Ph.D. thesis of Michael O'Neill, and reports a number of new directions in Grammatical Evolution research that are been conducted both within the confines of the University of Limerick's Biocomputing-Developmental Systems Centre where the book's authors reside, and also developments that are occurring through collaborations around the globe.",
Genetic Programming entries for Michael O'Neill Conor Ryan