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Multimodal Adaptive Graph Evolution for Program Synthesis

  • Conference paper
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Parallel Problem Solving from Nature – PPSN XVIII (PPSN 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15148))

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Abstract

Program synthesis constitutes a category of problems where the objective is to automatically produce computer programs that meet specified criteria. Among Genetic Programming algorithms, Cartesian Genetic Programming has been successfully used for a variety of function synthesis problems, such as circuit design, pattern analysis, and game playing. These problems are designed to work only on a single data type, for example, boolean values or entire images. Cartesian Genetic Programming cannot directly be applied to problems with multiple data types, which poses a great limitation, as more realistic programs should be able to deal with different data types. Mixed-Type Cartesian Genetic Programming is the only current extension of Cartesian Genetic Programming which allows for processing different data types. In this work, we present and study Multimodal Adaptive Graph Evolution, a multi-chromosome generalization of Cartesian Genetic Programming that groups functions by return type and constrains graph mutation based on node’s type coherence. We compare Multimodal Adaptive Graph Evolution to Mixed-Type Cartesian Genetic Programming on the Program Synthesis Benchmark Suite, showing that the representation and mutation constraints of Multimodal Adaptive Graph Evolution aid in the search of multimodal functions. Using Search Trajectory Networks, we find that Multimodal Adaptive Graph Evolution converges faster to a local or global minimum compared to Mixed-Type Cartesian Genetic Programming and explores the solution space more effectively by creating candidate solutions with lower semantic redundancy.

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Notes

  1. 1.

    https://github.com/camilodlt/PPSN_MAGE_PSB2_PBS.

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Acknowledgments

This project used compute resources from the CALMIP project P21049. This work is supported by the AI Interdisciplinary Institute ANITI, funded by the French program “Investing for the Future - PIA3” under Grant agreement no. ANR-19-PI3A-0004.

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Authors and Affiliations

  1. University of Toulouse - Institut de Recherche en Informatique de Toulouse (IRIT) - UMR5505, Toulouse, France

    Camilo De La Torre, Yuri Lavinas, Hervé Luga & Sylvain Cussat-Blanc

  2. Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France

    Kevin Cortacero

  3. ISAE-SUPAERO, University of Toulouse, Toulouse, France

    Dennis G. Wilson

Authors
  1. Camilo De La Torre
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  2. Yuri Lavinas
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  3. Kevin Cortacero
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  4. Hervé Luga
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  5. Dennis G. Wilson
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  6. Sylvain Cussat-Blanc
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Corresponding author

Correspondence to Camilo De La Torre .

Editor information

Editors and Affiliations

  1. University of Applied Sciences Upper Austria, Wels, Austria

    Michael Affenzeller

  2. University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan M. Winkler

  3. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  4. University of Paderborn, Paderborn, Germany

    Heike Trautmann

  5. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

  6. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  7. Leiden University, LEIDEN, The Netherlands

    Thomas Bäck

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De La Torre, C., Lavinas, Y., Cortacero, K., Luga, H., Wilson, D.G., Cussat-Blanc, S. (2024). Multimodal Adaptive Graph Evolution for Program Synthesis. In: Affenzeller, M., et al. Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, vol 15148. Springer, Cham. https://doi.org/10.1007/978-3-031-70055-2_19

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  • DOI: https://doi.org/10.1007/978-3-031-70055-2_19

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