Attributed grammatical evolution using shared memory spaces and dynamically typed semantic function specification

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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.

Original languageEnglish
Title of host publicationGenetic Programming - 18th European Conference, EuroGP 2015, Proceedings
EditorsPablo García-Sánchez, Penousal Machado, Sebastian Risi, Malcolm I. Heywood, Paolo Burelli, James McDermott, Kevin Sim, Mauro Castelli
PublisherSpringer Verlag
Pages105-112
Number of pages8
ISBN (Electronic)9783319165004
DOIs
Publication statusPublished - 2015
Event18th European Conference on Genetic Programming, EuroGP 2015 - Copenhagen, Denmark
Duration: 8 Apr 201510 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9025
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th European Conference on Genetic Programming, EuroGP 2015
Country/TerritoryDenmark
CityCopenhagen
Period8/04/1510/04/15

Keywords

  • Attribute grammars
  • Grammatical Evolution
  • Symbolic regression

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