@inproceedings{2dbe5cf2720f4765b65a02359b09aec6,
title = "Attributed grammatical evolution using shared memory spaces and dynamically typed semantic function specification",
abstract = "In this paper we introduce a new Grammatical Evolution (GE) system designed to support the specification of problem semantics in the form of attribute grammars (AG). We discuss the motivations behind our system design, from its use of shared memory spaces for attribute storage to the use of a dynamically type programming language, Python, to specify grammar semantics. After a brief analysis of some of the existing GE AG system we outline two sets of experiments carried out on four symbolic regression type (SR) problems. The first set using a context free grammar (CFG) and second using an AG. After presenting the results of our experiments we highlight some of the potential areas for future performance improvements, using the new functionality that access to Python interpreter and storage of attributes in shared memory space provides.",
keywords = "Attribute grammars, Grammatical Evolution, Symbolic regression",
author = "Patten, {James Vincent} and Conor Ryan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 18th European Conference on Genetic Programming, EuroGP 2015 ; Conference date: 08-04-2015 Through 10-04-2015",
year = "2015",
doi = "10.1007/978-3-319-16501-1_9",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "105--112",
editor = "Pablo Garc{\'i}a-S{\'a}nchez and Penousal Machado and Sebastian Risi and Heywood, {Malcolm I.} and Paolo Burelli and James McDermott and Kevin Sim and Mauro Castelli",
booktitle = "Genetic Programming - 18th European Conference, EuroGP 2015, Proceedings",
}