Efficient simulation of tissue-like P systems by transition cell-like P systems

  • Authors:

  • Daniel Díaz-Pernil;Mario J. Pérez-Jiménez;Álvaro Romero-Jiménez

  • Affiliations:

  • Department of Computer Science and Artificial Intelligence, Research Group on Natural Computing, University of Sevilla, Sevilla, Spain;Department of Computer Science and Artificial Intelligence, Research Group on Natural Computing, University of Sevilla, Sevilla, Spain;Department of Computer Science and Artificial Intelligence, Research Group on Natural Computing, University of Sevilla, Sevilla, Spain

  • Venue:
  • Natural Computing: an international journal
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

In the framework of P systems, it is known that the construction of exponential number of objects in polynomial time is not enough to efficiently solve NP-complete problems. Nonetheless, it could be sufficient to create an exponential number of membranes in polynomial time. Working with P systems whose membrane structure does not increase in size, it is known that it is not possible to solve computationally hard problems (unless P = NP), basically due to the impossibility of constructing exponential number of membranes, in polynomial time, using only evolution, communication and dissolution rules. In this paper we show how a family of recognizer tissue P systems with symport/antiport rules which solves a decision problem can be efficiently simulated by a family of basic recognizer P systems solving the same problem. This simulation allows us to transfer the result about the limitations in computational power, from the model of basic cell-like P systems to this kind of tissue-like P systems.