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Evolving Critical Boolean Networks

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Artificial Life and Evolutionary Computation (WIVACE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 900))

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Abstract

Random Boolean networks are a widely acknowledged model for cell dynamics. Previous studies have shown the possibility of achieving Boolean networks with given characteristics by means of evolutionary techniques. In this work we make a further step towards more biologically plausible models by aiming at evolving networks with a given fraction of active nodes along the attractors, while constraining the evolutionary process to move across critical networks. Results show that this path along criticality does not impede to climb the mount of improbable, yet biologically realistic requirements.

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Notes

  1. 1.

    In other words, we aim to obtain a divergence between the grey level and the Boolean functions’ bias, being not interested in reaching a particular final grey level

  2. 2.

    The position of these mutations within the string coding the individuals is randomly chosen, whereas their direction (0 → 1 or 1 → 0) is predisposed to reach the desired bias.

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Authors and Affiliations

  1. Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Modena, Italy

    Salvatore Magrì, Marco Villani & Roberto Serra

  2. European Centre for Living Technology, Venice, Italy

    Marco Villani & Roberto Serra

  3. Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

    Andrea Roli

  4. Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands

    Roberto Serra

Authors
  1. Salvatore Magrì
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  2. Marco Villani
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  3. Andrea Roli
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  4. Roberto Serra
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Corresponding authors

Correspondence to Salvatore Magrì or Marco Villani .

Editor information

Editors and Affiliations

  1. University of Parma, Parma, Italy

    Stefano Cagnoni

  2. University of Parma, Parma, Italy

    Monica Mordonini

  3. Università degli Studi eCampus, Novedrate, Italy

    Riccardo Pecori

  4. University of Bologna, Cesena, Italy

    Andrea Roli

  5. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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Magrì, S., Villani, M., Roli, A., Serra, R. (2019). Evolving Critical Boolean Networks. In: Cagnoni, S., Mordonini, M., Pecori, R., Roli, A., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2018. Communications in Computer and Information Science, vol 900. Springer, Cham. https://doi.org/10.1007/978-3-030-21733-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-21733-4_2

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