Grammatical Evolution - Published by Kluwer in 2003 Grammatical Evolution the book is based on the PhD thesis of Dr. Michael O'Neill (UCD NCRA Director) and 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 behaviour 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 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, and 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.

http://www.grammatical-evolution.org

A number of significant extensions to Grammatical Evolution are being investigated in the UCD Natural Computing Research and Applications Group.



Applications of Evolutionary Design (App'ED) (2014-)
One of the greatest designers we know of is the natural process of biological evolution, which has designed living organisms of amazing variety and complexity, with powerful problem solving and survival tools such as the use of a societies and the nervous system. An artificial evolutionary process has been captured, albeit crudely, by computer scientists. While impressive results have been achieved, they have struggled to produce the complexity witnessed in nature. We will develop automated design tools inspired by the natural world, and apply them to two areas of national importance: Communications Networks with Bell Labs Ireland and eGaming. The developed tools have potential for even broader application to a diverse set of design problem domains (e.g., renewable energy engineering, bioengineering, synthetic biology, and software). The project is funded under an SFI Principal Investigator award (13/IA/1850).



Evolution in Dynamic Environments with Grammatical Evolution (EDGE) (2009-2014)
The primary objective of the EDGE project is to analyse and improve the ability of Grammatical Evolution (GE) to find solutions in dynamic environments, facilitating its application to hard real-world dynamic problems. Many of the most challenging problems facing researchers and decision makers are those with a dynamic nature. That is, the environment in which the solution exists, and consequently the optimal solution itself, changes over time. The process of evolution has been particularly successful at producing organisms that can survive and adapt to ever-changing environments. Inspired by the workings of biological processes, this project develops a novel method and associated sofware tools which can be applied to solve hard dynamic problems. The project is funded under an SFI Principal Investigator award (08/IN.1/I1868). More details on EDGE are available on the project page.

Meta-Grammars are proposed as a novel approach to improve the evolvability of the grammar-based Genetic Programming approach of Grammatical Evolution. This will allow Grammatical Evolution to be more effectively applied to non-stationary (dynamic) problem domains such as those found in Bioinformatics (e.g. time series analysis of microarrays and massively parallel signature sequencing), telecommunications (e.g. call routing), and financial modelling (e.g. risk management systems). Science Foundation Ireland are funding two PhD studentships on this project under a Research Frontiers award.

Saoirse Amarteifio pursued an MSc by Research in this area, graduating in September 2005. His thesis is available here.

Abstract: A novel XML implementation of Grammatical Evolution is developed. This has a number of interesting features such as the use of XSLT for genetic operators and the use of reflection to build an object tree from an XML expression tree. This framework is designed to be used for remote or local evaluation of evolved program structures and provides a number of abstraction layers for program evaluation and evolution. A dynamical swarm system is evolved as a special-case function induction problem to illustrate the application of XMLGE. Particle behaviours are evolved to optimize colony performance. A dual process evolutionary algorithm based on the immune system using rich representations is developed. A dual process feature detection and feature integration model is described and the performance shown on benchmark GP problems. An adaptive feature detection method uses coevolving XPath antibodies to take selective interest in primary structures. Grammars are used to generate reciprocal binding structures (antibodies) given any primary domain grammar. A codon compression algorithm is developed which shows performance improvements on symbolic regression and multiplexer problems. The algorithm is based on questions about the information content of a genome. This also exploits information from the rich representation of XMLGE.

UCD's NCRA have developed and maintain a version of Grammatical Evolution in Java GEVA.



There are a large number of papers published by UCD's NCRA on Grammatical Evolution in recent years, these can be found on this website and www.grammatical-evolution.org.