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Evolutionary Optimization of Graphs with GraphEA

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AIxIA 2020 – Advances in Artificial Intelligence (AIxIA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12414))

Abstract

Many practically relevant computing artifacts are forms of graphs, as, e.g., neural networks, mathematical expressions, finite automata. This great generality of the graph abstraction makes it desirable a way for searching in the space of graphs able to work effectively regardless of the graph form and application. In this paper, we propose GraphEA, a modular evolutionary algorithm (EA) for evolving graphs. GraphEA is modular in the sense that it can be tailored to any graph optimization task by providing components for specifying how to vary edges and nodes, and how to check the validity of a graph. We designed GraphEA by building on previous EAs dealing with particular kinds of graphs and included a speciation mechanism for the preservation of structural innovations and one for the gradual complexification of the solutions structure. To validate the generality of GraphEA, we applied it to 3 radically different tasks (regressions, in two flavors, text extraction from examples, evolution of controllers for soft robots) and compared its effectiveness against less general approaches. The results are promising and indicate some directions for further improvement of GraphEA.

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Acknowledgments

We thanks Luca Zanella for the CMA-ES implementation in JGEA. The experimental evaluation of this work has been done on CINECA HPC cluster within the CINECA-University of Trieste agreement.

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Authors and Affiliations

  1. Department of Engineering and Architecture, University of Trieste, Trieste, Italy

    Eric Medvet & Alberto Bartoli

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  1. Eric Medvet
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  2. Alberto Bartoli
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Correspondence to Eric Medvet .

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Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Department of Informatics, Systems and C, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

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Medvet, E., Bartoli, A. (2021). Evolutionary Optimization of Graphs with GraphEA. In: Baldoni, M., Bandini, S. (eds) AIxIA 2020 – Advances in Artificial Intelligence. AIxIA 2020. Lecture Notes in Computer Science(), vol 12414. Springer, Cham. https://doi.org/10.1007/978-3-030-77091-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-77091-4_6

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  • Print ISBN: 978-3-030-77090-7

  • Online ISBN: 978-3-030-77091-4

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