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Towards Quantitative Networks of Polarized Evolutionary Processors: A Bio-Inspired Computational Model with Numerical Evaluations

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Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection (PAAMS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 473))

Abstract

Networks of Polarized Evolutionary Processors is a highly parallel distributed computing model inspired and abstracted from the biological evolution. This model is computationally complete and able to efficiently solve NP complete problems. Although this model is inspired from biology, basically it has been investigated from the points of view of mathematical and computer science goals with a qualitative perspective. It is true that Networks of Polarized Evolutionary Processors incorporate a numerical evaluation over the data that it processes, but this is not used from a quantitative viewpoint. In this paper we propose to enhance Networks of Polarized Evolutionary Processors of a quantitative perspective through a novel number of formal components. In particular, these components are able to evaluate quantitative conditions inherent to biological phenomena preserving the same computational power of Networks of Polarized Evolutionary Processors. Moreover, as a proof of concept, we model and simulate a simple but expressive example: a discrete abstraction of the sodium-potassium pump that includes the components proposed. Finally, we suggest that this integration enhances Networks of Polarized Evolutionary Processors model to (a) be more expressive for the algorithm design and (b) use less resources (nodes, rules, strings and computation time). This resource reduction could become a clear advantage when we will deploy hardware/software solutions of these bio-inspired computational models on top of massively distributed computational platforms.

A.O. de la Puente—Results partially supported by Gain Dynamics, project iQuest which funding is applied to the Fondo Europeo de Desarrollo Regional para el Fomento de Sectores Tecnológicos de la Comunidad de Madrid, within the Estrategia Regional de Investigación e Innovación para una Especialización Inteligente (RIS3) in the Programa Operativo de la Comunidad de Madrid 2014-2020

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Authors and Affiliations

  1. Department of Computer Systems, University College of Computer Science, Technical University of Madrid, Crta. de Valencia Km. 7, 28031, Madrid, Spain

    Sandra Gómez Canaval & Stanislav Vakaruk

  2. Departament of Computer Systems Engineering, Advanced Technical College, Autónoma of Madrid University, Campus of Cantoblanco, 28049, Madrid, Spain

    Karina Jiménez & Alfonso Ortega de la Puente

Authors
  1. Sandra Gómez Canaval
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  2. Karina Jiménez
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  3. Alfonso Ortega de la Puente
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  4. Stanislav Vakaruk
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Corresponding author

Correspondence to Alfonso Ortega de la Puente .

Editor information

Editors and Affiliations

  1. Dept de Infor y Auto Facultad de Ciencia, University of Salamanca, Salamanca, Spain

    Fernando de la Prieta

  2. ETS Ingeniería Informática, University of Sevilla, Sevilla, Spain

    María J. Escalona

  3. ETSI Informática, Universidad de Sevilla, Sevilla, Spain

    Rafael Corchuelo

  4. and Technology, Lille University of Science, Villeneuve d'Ascq Cédex, France

    Philippe Mathieu

  5. GECAD, Porto, Portugal

    Zita Vale

  6. School of Computer Science, Dartmouth College, Hanover, USA

    Andrew T. Campbell

  7. Dept of Electrical Engineering and IT, University of Naples Federico II, Naples, Italy

    Silvia Rossi

  8. LAMIH (UMR CNRS 8530), Universite de Valenciennes, Valenciennes, France

    Emmanuel Adam

  9. Campus Catalunya, Universitat Rovira i Virgili, Tarragona, Spain

    María D. Jiménez-López

  10. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena M. Navarro

  11. Departamento de Informática y Automática, University of Salamanca, Salamanca, Salamanca, Spain

    María N. Moreno

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© 2016 Springer International Publishing Switzerland

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Canaval, S.G., Jiménez, K., de la Puente, A.O., Vakaruk, S. (2016). Towards Quantitative Networks of Polarized Evolutionary Processors: A Bio-Inspired Computational Model with Numerical Evaluations. In: de la Prieta, F., et al. Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. PAAMS 2016. Advances in Intelligent Systems and Computing, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-319-40159-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-40159-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40158-4

  • Online ISBN: 978-3-319-40159-1

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