Atkinson, Timothy, Plump, Detlef orcid.org/0000-0002-1148-822X and Stepney, Susan orcid.org/0000-0003-3146-5401 (2018) Evolving Graphs by Graph Programming. In: Cagnoni, Stefano, Zhang, Mengjie, Garcia-Sanchez, Pablo, Castelli, Mauro and Sekanina, Lukas, (eds.) Genetic Programming - 21st European Conference, EuroGP 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , pp. 35-51.
Abstract
Rule-based graph programming is a deep and rich topic. We present an approach to exploiting the power of graph programming as a representation and as an execution medium in an evolutionary algorithm (EGGP). We demonstrate this power in comparison with Cartesian Genetic Programming (CGP), showing that it is significantly more efficient in terms of fitness evaluations on some classic benchmark problems. We hypothesise that this is due to its ability to exploit the full graph structure, leading to a richer mutation set, and outline future work to test this hypothesis, and to exploit further the power of graph programming within an EA.
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Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer-Verlag Berlin Heidelberg 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 22 Jan 2018 14:50 |
Last Modified: | 16 Oct 2024 10:56 |
Published Version: | https://doi.org/10.1007/978-3-319-77553-1_3 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-319-77553-1_3 |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126500 |
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