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Multimodal Adaptive Graph Evolution

Published: 01 August 2024 Publication History

Abstract

The problem of program synthesis involves automatically finding a function based on evaluation criteria, like matching input-output pairs. While Cartesian Genetic Programming (CGP) has excelled in various function synthesis tasks, it has primarily been limited to single data types, hindering its applicability to diverse data. Mixed-Type CGP, proposed in 2012, aimed to address this limitation but faced challenges due to search space limitations and complexity in building function libraries. In this study, we introduce Multimodal Adaptive Graph Evolution (MAGE), a generalized CGP extension that integrates functions of different data types by grouping them accordingly and imposing mutation constraints based on type. Through comparisons with standard CGP and Mixed-Type CGP on Program Synthesis Benchmark and image classification tasks, we demonstrate that MAGE's representation and mutation constraints facilitate the search for multimodal functions.

References

[1]
Kévin Cortacero, Brienne McKenzie, Sabina Müller, Roxana Khazen, Fanny Lafouresse, Gaëlle Corsaut, Nathalie Van Acker, François-Xavier Frenois, Laurence Lamant, Nicolas Meyer, Béatrice Vergier, Dennis G. Wilson, Hervé Luga, Oskar Staufer, Michael L. Dustin, Salvatore Valitutti, and Sylvain Cussat-Blanc. 2023. Evolutionary design of explainable algorithms for biomedical image segmentation. Nature Communications 14, 1 (Nov. 2023), 7112. https://www.nature.com/articles/s41467-023-42664-x Number: 1 Publisher: Nature Publishing Group.
[2]
Simon Harding, Vincent Graziano, Jürgen Leitner, and Jürgen Schmidhuber. 2012. MT-CGP: mixed type cartesian genetic programming. In Proceedings of the 14th annual conference on Genetic and evolutionary computation (GECCO '12). Association for Computing Machinery, New York, NY, USA, 751--758.
[3]
Simon Harding, Jürgen Leitner, and Jürgen Schmidhuber. 2013. Cartesian Genetic Programming for Image Processing. In Genetic Programming Theory and Practice X, Rick Riolo, Ekaterina Vladislavleva, Marylyn D Ritchie, and Jason H. Moore (Eds.). Springer, New York, NY.
[4]
Thomas Helmuth and Peter Kelly. 2021. PSB2: the second program synthesis benchmark suite. In Proceedings of the Genetic and Evolutionary Computation Conference (Lille, France) (GECCO '21). Association for Computing Machinery, New York, NY, USA, 785--794.
[5]
John R. Koza. 1994. Genetic programming as a means for programming computers by natural selection. Statistics and Computing 4, 2 (June 1994), 87--112.
[6]
Matthieu Macret and Philippe Pasquier. 2014. Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO '14). Association for Computing Machinery, New York, NY, USA, 309--316.
[7]
Julian Francis Miller. 2020. Cartesian genetic programming: its status and future. Genetic Programming and Evolvable Machines 21, 1 (June 2020), 129--168.
[8]
Julian F. Miller and Peter Thomson. 2000. Cartesian Genetic Programming. In Genetic Programming (Lecture Notes in Computer Science), Riccardo Poli, Wolfgang Banzhaf, William B. Langdon, Julian Miller, Peter Nordin, and Terence C. Fogarty (Eds.). Springer, Berlin, Heidelberg.
[9]
Lee Spector, Jon Klein, and Maarten Keijzer. 2005. The Push3 execution stack and the evolution of control. In Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (Washington DC, USA) (GECCO '05). Association for Computing Machinery, New York, NY, USA, 1689--1696.
[10]
Lee Spector and Alan Robinson. 2002. Genetic Programming and Autoconstructive Evolution with the Push Programming Language. Genetic Programming and Evolvable Machines 3, 1 (March 2002), 7--40.
[11]
Carsen Stringer, Tim Wang, Michalis Michaelos, and Marius Pachitariu. 2021. Cellpose: a generalist algorithm for cellular segmentation. Nature methods 18, 1 (2021), 100--106.
[12]
Dennis G Wilson, Sylvain Cussat-Blanc, Hervé Luga, and Julian F Miller. 2018. Evolving simple programs for playing atari games. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18). Association for Computing Machinery, New York, NY, USA.

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cover image ACM Conferences
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2024
2187 pages
ISBN:9798400704956
DOI:10.1145/3638530
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Published: 01 August 2024

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  1. genetic programming
  2. evolutionary computation
  3. symbolic regression

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