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

  • Sandra Gómez Canaval
  • Karina Jiménez
  • Alfonso Ortega de la PuenteEmail author
  • Stanislav Vakaruk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Output Node Valuation Mapping Communication Step Quantitative Condition Quantitative Perspective 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sandra Gómez Canaval
    • 1
  • Karina Jiménez
    • 2
  • Alfonso Ortega de la Puente
    • 2
    Email author
  • Stanislav Vakaruk
    • 1
  1. 1.Department of Computer Systems, University College of Computer ScienceTechnical University of MadridMadridSpain
  2. 2.Departament of Computer Systems Engineering, Advanced Technical CollegeAutónoma of Madrid UniversityMadridSpain

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