Time‐Free Solution to 3‐Coloring Problem Using Tissue P Systems

Niu, Y.; Xiao, Jianhua; Jiang, Yun

doi:10.1049/cje.2016.05.003

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The concept of time-free was first introduced in [17], and the open problems with solutions for NP-complete problems were formulated in [18]. Recently, some NP-complete problems have been solved in a time-free manner [19][20][21][22][23]. However, the model of these P systems is based on cell-like P systems.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Time‐Free Solution to SAT Problem by Tissue P Systems

Luo

Xiong

Zhang

2017

Mathematical Problems in Engineering

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Tissue P systems are a class of computing models inspired by intercellular communication, where the rules are used in the nondeterministic maximally parallel manner. As we know, the execution time of each rule is the same in the system. However, the execution time of biochemical reactions is hard to control from a biochemical point of view. In this work, we construct a uniform and efficient solution to the SAT problem with tissue P systems in a time-free way for the first time. With the P systems constructed from the sizes of instances, the execution time of the rules has no influence on the computation results. As a result, we prove that such system is shown to be highly effective for NP-complete problem even in a time-free manner with communication rules of length at most 3.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Time‐Free Solution to SAT Problem by Tissue P Systems

Luo

Xiong

Zhang

2017

Mathematical Problems in Engineering

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“…In some studies, spiking-like P systems are used as a computing device to solve several arithmetic problems; for instance, the addition of n natural numbers and the product of two arbitrary natural numbers with a given length of binary bits [24,25]. Of course, apart from providing automatic design and arithmetic operations, P systems are applied to hard problems such as vertex cover [26,27], quadratic assignment [28], graph coloring problems [29][30][31], and non-semilinear sets [32,33]. Besides, they have been applied to solve image processing problems [34][35][36][37][38][39], complex optimization problems [40][41][42], complex market interactions [43], and intelligent control problems for robots [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

A Review of Membrane Computing Models for Complex Ecosystems and a Case Study on a Complex Giant Panda System

Duan

Rong

et al. 2020

Complexity

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Ecosystem modelling based on membrane computing is emerging as a powerful way to study the dynamics of (real) ecological populations. These models, providing distributed parallel devices, have shown a great potential to imitate the rich features observed in the behaviour of species and their interactions and key elements to understand and model ecosystems. Compared with differential equations, membrane computing models, also known as P systems, can model more complex biological phenomena due to their modularity and their ability to enclose the evolution of different environments and simulate, in parallel, different interrelated processes. In this paper, a comprehensive survey of membrane computing models for ecosystems is given, taking a giant panda ecosystem as an example to assess the model performance. This work aims at modelling a number of species using P systems with different membrane structure types to predict the number of individuals depending on parameters such as reproductive rate, mortality rate, and involving processes as rescue or release. Firstly, the computing models are introduced conceptually, describing the main elements constituting the syntax of these systems and explaining the semantics of the rules involved. Next, various modelled species (including endangered animals, plants, and bacteria) are summarized, and some computer tools are presented. Then, a discussion follows on the use of P systems for ecosystem modelling. Finally, a case study on giant pandas in Chengdu Base is analysed, concluding that the study in this field by using PDP systems can provide a valuable tool to deepen into the knowledge about the evolution of the population. This could ultimately help in the decision-making processes of the managers of the ecosystem to increase the species diversity and modify the adaptability. Besides, the impacts of natural disasters on the population dynamics of the species should also be considered. The analysis performed throughout the paper has taken into consideration this fact in order to increase the reliability of the prospects making use of the models designed.

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