Tuning Frontiers of Efficiency in Tissue P Systems with Evolutional Communication Rules. (28th April 2021)

• Record Type: Journal Article
• Authors: Orellana-Martín, David; Valencia-Cabrera, Luis; Song, Bosheng; Pan, Linqiang; Pérez-Jiménez, Mario J.
• Other Names: Cherifi Hocine Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-04-28
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/7120840
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Over the last few years, a new methodology to address the P versus NP problem has been developed, based on searching for borderlines between the nonefficiency of computing models (only problems in class P can be solved in polynomial time) and the presumed efficiency (ability to solve NP -complete problems in polynomial time). These borderlines can be seen as frontiers of efficiency, which are crucial in this methodology. "Translating, " in some sense, an efficient solution in a presumably efficient model to an efficient solution in a nonefficient model would give an affirmative answer to problem P versus NP . In the framework of Membrane Computing, the key of this approach is to detect the syntactic or semantic ingredients that are needed to pass from a nonefficient class of membrane systems to a presumably efficient one. This paper deals with tissue P systems with communication rules of type symport/antiport allowing the evolution of the objects triggering the rules. In previous works, frontiers of efficiency were found in these kinds of membrane systems both with division rules and with separation rules. However, since they were not optimal, it is interesting to refine these frontiers. In this work, optimal frontiers of the efficiency are obtained in terms of the total number of objects involved in the communication rules used for that kind of membrane systems. These optimizations could be easier to translate, if possible, to efficient solutions in a nonefficient model.