Related
Ontology
Subjects/Areas/Topics:Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Hybrid Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing

Abstract: This paper describes an evolutionary computation based graph rewriting approach to generating classes of
graphs that exhibit a set of desired global features. A set of rules are used to generate, in a constructive
manner, classes of graphs. Each rule represents a transformation from one graph to another. Each of these
transformations causes local changes in the graph. Probabilities can be assigned to the rules which govern the
frequency with which they will be applied. By assigning these probabilities correctly, one can generate graphs
exhibiting desirable global features. However, choosing the correct probability distribution to generate the
desired graphs is not an easy task for certain graphs and the task of finding the correct settings for these graphs
may represent a difficult search space for the evolutionary algorithms. In order to generate graphs exhibiting
desirable features, an evolutionary algorithm is used to find the suitable probabilities to assign to the rules.
The fitness function rewards graphs that exhibit the desired properties. We show, using a small rule base, how
a range of graphs can be generated.(More)

This paper describes an evolutionary computation based graph rewriting approach to generating classes of graphs that exhibit a set of desired global features. A set of rules are used to generate, in a constructive manner, classes of graphs. Each rule represents a transformation from one graph to another. Each of these transformations causes local changes in the graph. Probabilities can be assigned to the rules which govern the frequency with which they will be applied. By assigning these probabilities correctly, one can generate graphs exhibiting desirable global features. However, choosing the correct probability distribution to generate the desired graphs is not an easy task for certain graphs and the task of finding the correct settings for these graphs may represent a difficult search space for the evolutionary algorithms. In order to generate graphs exhibiting desirable features, an evolutionary algorithm is used to find the suitable probabilities to assign to the rules. The fitness function rewards graphs that exhibit the desired properties. We show, using a small rule base, how a range of graphs can be generated.

Guests can use SciTePress Digital Library without having a SciTePress account. However, guests have limited access to downloading full text versions of papers and no access to special options.

Guests can use SciTePress Digital Library without having a SciTePress account. However, guests have limited access to downloading full text versions of papers and no access to special options.

Barry, A.; Griffith, J. and O'Riordan, C. (2015). An Evolutionary and Graph-Rewriting based Approach to Graph Generation.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 237-243. DOI: 10.5220/0005597102370243

@conference{ecta15, author={Aaron Barry. and Josephine Griffith. and Colm O'Riordan.}, title={An Evolutionary and Graph-Rewriting based Approach to Graph Generation}, booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,}, year={2015}, pages={237-243}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0005597102370243}, isbn={978-989-758-157-1}, }

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, TI - An Evolutionary and Graph-Rewriting based Approach to Graph Generation SN - 978-989-758-157-1 AU - Barry, A. AU - Griffith, J. AU - O'Riordan, C. PY - 2015 SP - 237 EP - 243 DO - 10.5220/0005597102370243