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Attribute grammar evolution

Author
Cruz Echeandía, Marina de la; Ortega de la Puente, Alfonsountranslated; Alfonseca, Manuel
Entity
UAM. Departamento de Ingeniería Informática
Publisher
Springer Berlin Heidelberg
Date
2005
Citation
Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Spain, June 15-18, 2005, Proceedings. Part II. Lecture Notes in Computer Science, Volumen 3562. Springer, 2005. 182-191.
ISSN
0302-9743 (print); 1611-3349 (online)
ISBN
978-3-540-26319-7 (print); 978-3-540-31673-2 (online)
DOI
10.1007/11499305_19
Editor's Version
http://dx.doi.org/10.1007/11499305_19
Subjects
Computation by Abstract Devices; Algorithm Analysis and Problem Complexity; Artificial Intelligence; Pattern Recognition; Evolutionary Biology; Image Processing; Computer Vision; Informática
URI
http://hdl.handle.net/10486/664129
Note
The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_19
Proceedings of First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, June 15-18, 2005
Rights
© Springer-Verlag Berlin Heidelberg 2005

Abstract

This paper describes Attribute Grammar Evolution (AGE), a new Automatic Evolutionary Programming algorithm that extends standard Grammar Evolution (GE) by replacing context-free grammars by attribute grammars. GE only takes into account syntactic restrictions to generate valid individuals. AGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. Attribute grammars make it possible to semantically describe the solution. The paper shows empirically that AGE is as good as GE for a classical problem, and proves that including semantics in the grammar can improve GE performance. An important conclusion is that adding too much semantics can make the search difficult.
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