scholarly journals Time-Free Solution to SAT Problem by Tissue P Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yueguo Luo ◽  
Zhongyang Xiong ◽  
Guanghua Zhang
Keyword(s):  
Intercellular Communication ◽  
Execution Time ◽  
Efficient Solution ◽  
Point Of View ◽  
P Systems ◽  
Free Solution ◽  
Complete Problem ◽  
Np Complete ◽  
First Time ◽  
Computing Models

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.

Time-Free Solution for QSAT by Using Timed Tissue P Systems

2014 ◽  
Vol 568-570 ◽  
pp. 812-816 ◽  
Author(s):  
Yun Yun Niu ◽  
Zhi Gao Wang
Keyword(s):  
Computational Efficiency ◽  
Execution Time ◽  
Realistic Model ◽  
Point Of View ◽  
P Systems ◽  
P System ◽  
Free Solution ◽  
Complete Problem ◽  
Computational Processes ◽  
Tissue P System

A timed tissue P system is constructed by adding a time mapping to the rules of tissue P system to specify the execution time for each rule. It is a more realistic model from a biological point of view. In this study, we investigate the computational efficiency of timed tissue P systems. A uniform and time-free solution to QSAT problem, a famous PSPACE-complete problem, is proposed, where the execution time of the computational processes involved can vary arbitrarily and the output produced is always the same.

scholarly journals Tuning Frontiers of Efficiency in Tissue P Systems with Evolutional Communication Rules

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
David Orellana-Martín ◽  
Luis Valencia-Cabrera ◽  
Bosheng Song ◽  
Linqiang Pan ◽  
Mario J. Pérez-Jiménez
Keyword(s):  
Polynomial Time ◽  
Efficient Solution ◽  
Efficient Solutions ◽  
Affirmative Answer ◽  
P Systems ◽  
Membrane Systems ◽  
Complete Problems ◽  
Np Complete ◽  
Np Problem ◽  
Computing Models

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.

scholarly journals Time-Free Solution to Hamilton Path Problems Using P Systems withd-Division

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Tao Song ◽  
Xun Wang ◽  
Hongjiang Zheng
Keyword(s):  
Execution Time ◽  
Linear Time ◽  
Hamiltonian Path ◽  
P Systems ◽  
Free Solution ◽  
Hamilton Path ◽  
Working Space ◽  
A Cell ◽  
Hard Problems ◽  
Computing Models

P systems withd-division are a particular class of distributed and parallel computing models investigated in membrane computing, which are inspired from the budding behavior of Baker’s yeast (a cell can generate several cells in one reproducing cycle). In previous works, such systems can theoretically generate exponential working space in linear time and thus provide a way to solve computational hard problems in polynomial time by a space-time tradeoff, where the precise execution time of each evolution rule, one time unit, plays a crucial role. However, the restriction that each rule has a precise same execution time does not coincide with the biological fact, since the execution time of biochemical reactions can vary because of external uncontrollable conditions. In this work, we consider timed P systems withd-division by adding a time mapping to the rules to specify the execution time for each rule, as well as the efficiency of the systems. As a result, a time-free solution to Hamiltonian path problem (HPP) is obtained by a family of such systems (constructed in a uniform way), that is, the execution time of the rules (specified by different time mappings) has no influence on the correctness of the solution.

The computational power of timed P systems with active membranes using promoters

2018 ◽  
Vol 29 (5) ◽  
pp. 663-680 ◽  
Author(s):  
YUEGUO LUO ◽  
HAIJUN TAN ◽  
YING ZHANG ◽  
YUN JIANG
Keyword(s):  
P Systems ◽  
P System ◽  
Computational Power ◽  
Computable Set ◽  
Complete Problem ◽  
Active Membranes ◽  
New Variant ◽  
Uniform Solution ◽  
Bioinspired Computing ◽  
Computing Models

P systems with active membranes are a class of bioinspired computing models, where the rules are used in the non-deterministic maximally parallel manner. In this paper, first, a new variant of timed P systems with active membranes is proposed, where the application of rules can be regulated by promoters with only two polarizations. Next, we prove that any Turing computable set of numbers can be generated by such a P system in the time-free way. Moreover, we construct a uniform solution to the$\mathcal{SAT}$problem in the framework of such recognizer timed P systems in polynomial time, and the feasibility and effectiveness of the proposed system is demonstrated by an instance. Compared with the existing methods, the P systems constructed in our work require fewer necessary resources and RS-steps, which show that the solution is effective toNP-complete problem.

scholarly journals Anti-unification in Constraint Logic Programming

2019 ◽  
Vol 19 (5-6) ◽  
pp. 773-789 ◽  
Author(s):  
GONZAGUE YERNAUX ◽  
WIM VANHOOF
Keyword(s):  
Logic Programming ◽  
Execution Time ◽  
Constraint Logic Programming ◽  
Complete Problem ◽  
Maximal Amount ◽  
Np Complete ◽  
General Goal ◽  
Shared Structure

AbstractAnti-unification refers to the process of generalizing two (or more) goals into a single, more general, goal that captures some of the structure that is common to all initial goals. In general one is typically interested in computing what is often called a most specific generalization, that is a generalization that captures a maximal amount of shared structure. In this work we address the problem of anti-unification in CLP, where goals can be seen as unordered sets of atoms and/or constraints. We show that while the concept of a most specific generalization can easily be defined in this context, computing it becomes an NP-complete problem. We subsequently introduce a generalization algorithm that computes a well-defined abstraction whose computation can be bound to a polynomial execution time. Initial experiments show that even a naive implementation of our algorithm produces acceptable generalizations in an efficient way.

scholarly journals Solving Multidimensional 0-1 Knapsack Problem with Time-Free Tissue P Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xiangrong Liu ◽  
Ziming Li ◽  
Juan Suo ◽  
Ying Ju ◽  
Juan Liu ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Knapsack Problem ◽  
Execution Time ◽  
Efficient Solution ◽  
P Systems ◽  
Distributed Model ◽  
P System ◽  
Biological Processes ◽  
Execution Times ◽  
Tissue P System

Tissue P system is a class of parallel and distributed model; a feature of traditional tissue P system is that the execution time of certain biological processes is very sensitive to environmental factors that might be hard to control. In this work, we construct a family of tissue P systems that works independently from the values associated with the execution times of the rules. Furthermore, we present a time-free efficient solution to multidimensional 0-1 knapsack problem by timed recognizer tissue P systems.

scholarly journals Hybrid Scheduling Strategy in Cloud Computing based on Optimization Algorithms

2021 ◽  
Vol 4 (1) ◽  
pp. 226-234
Author(s):  
Komal . ◽  
Gaurav Goel ◽  
Milanpreet Kaur
Keyword(s):  
Cloud Computing ◽  
Execution Time ◽  
Cloud Service ◽  
Previous Method ◽  
Optimization Techniques ◽  
Energy Usage ◽  
Complete Problem ◽  
Mutation Operators ◽  
Np Complete ◽  
Time And Energy

As a platform for offering on-demand services, cloud computing has increased in relevance and appeal. It has a pay-per-use model for its services. A cloud service provider's primary goal is to efficiently use resources by reducing execution time, cost, and other factors while increasing profit. As a result, effective scheduling algorithms remain a key issue in cloud computing, and this topic is categorized as an NP-complete problem. Researchers previously proposed several optimization techniques to address the NP-complete problem, but more work is needed in this area. This paper provides an overview of strategy for successful task scheduling based on a hybrid heuristic approach for both regular and larger workloads. The previous method handles the jobs adequately, but its performance degrades as the task size becomes larger. The proposed optimum scheduling method employs two distinct techniques to select the suitable VM for the specified job. To begin, it enhances the LJFP method by employing OSIG, an upgraded version of the Genetic Algorithm, to choose solutions with improved fitness factors, crossover, and mutation operators. This selection returns the best machines, and PSO then chooses one for a specific job. The appropriate machine is chosen depending on several factors, including the expected execution time, current load, and energy usage. The proposed algorithm's performance is assessed using two distinct cloud scenarios with various VMs and tasks, and overall execution time and energy usage are calculated. The proposed algorithm outperforms existing techniques in terms of energy and average execution time usage in both scenarios.

A time-free uniform solution to subset sum problem by tissue P systems with cell division

2015 ◽  
Vol 27 (1) ◽  
pp. 17-32 ◽  
Author(s):  
BOSHENG SONG ◽  
TAO SONG ◽  
LINQIANG PAN
Keyword(s):  
Cell Division ◽  
Execution Time ◽  
P Systems ◽  
Subset Sum Problem ◽  
Biological Inspiration ◽  
Global Clock ◽  
Subset Sum ◽  
Uniform Solution ◽  
Hard Problems ◽  
Computing Models

Tissue P systems are a class of bio-inspired computing models motivated by biochemical interactions between cells in a tissue-like arrangement. Tissue P systems with cell division offer a theoretical device to generate an exponentially growing structure in order to solve computationally hard problems efficiently with the assumption that there exists a global clock to mark the time for the system, the execution of each rule is completed in exactly one time unit. Actually, the execution time of different biochemical reactions in cells depends on many uncertain factors. In this work, with this biological inspiration, we remove the restriction on the execution time of each rule, and the computational efficiency of tissue P systems with cell division is investigated. Specifically, we solve subset sum problem by tissue P systems with cell division in a time-free manner in the sense that the correctness of the solution to the problem does not depend on the execution time of the involved rules.

scholarly journals P Systems with Evolutional Communication and Division Rules

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 327
Author(s):  
David Orellana-Martín ◽  
Luis Valencia-Cabrera ◽  
Mario J. Pérez-Jiménez
Keyword(s):  
Computational Complexity ◽  
Complexity Theory ◽  
Membrane Computing ◽  
P Systems ◽  
Membrane Systems ◽  
Sat Problem ◽  
Complete Problem ◽  
Intractable Problems ◽  
Np Complete ◽  
The One

A widely studied field in the framework of membrane computing is computational complexity theory. While some types of P systems are only capable of efficiently solving problems from the class P, adding one or more syntactic or semantic ingredients to these membrane systems can give them the ability to efficiently solve presumably intractable problems. These ingredients are called to form a frontier of efficiency, in the sense that passing from the first type of P systems to the second type leads to passing from non-efficiency to the presumed efficiency. In this work, a solution to the SAT problem, a well-known NP-complete problem, is obtained by means of a family of recognizer P systems with evolutional symport/antiport rules of length at most (2,1) and division rules where the environment plays a passive role; that is, P systems from CDEC^(2,1). This result is comparable to the one obtained in the tissue-like counterpart, and gives a glance of a parallelism and the non-evolutionary membrane systems with symport/antiport rules.

scholarly journals Solution to PSPACE-Complete Problem Using P Systems with Active Membranes with Time-Freeness

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Bosheng Song ◽  
Yuan Kong
Keyword(s):  
Execution Time ◽  
Cell Structure ◽  
P Systems ◽  
Biological Information ◽  
Natural Computing ◽  
Boolean Satisfiability Problem ◽  
Active Membranes ◽  
Hard Problems ◽  
And Behavior ◽  
Computing Models

P systems with active membranes are powerful parallel natural computing models, which were inspired by cell structure and behavior. Inspired by the parallel processing of biological information and with the idealistic assumption that each rule is completed in exactly one time unit, P systems with active membranes are able to solve computational hard problems in a feasible time. However, an important biological fact in living cells is that the execution time of a biochemical reaction cannot be accurately divided equally and completed in one time unit. In this work, we consider time as an important factor for the computation in P systems with active membranes and investigate the computational efficiency of such P systems. Specifically, we present a time-free semiuniform solution to the quantified Boolean satisfiability problem (QSATproblem, for short) in the framework of P systems with active membranes, where the solution to such problem is correct, which does not depend on the execution time for the used rules.

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