Agustín Riscos-Núñez

An overview of p-lingua 2.0

P–Lingua is a programming language for membrane computing which aims to be a standard to define P systems. In order to implement this idea, a Java library called pLinguaCore has been developed as a software framework for cell–like P systems. It is able to handle input files (either in XML or in P–Lingua format) defining P systems from a number of different cell–like P system models. Moreover, the library includes several built–in simulators for each supported model. For the sake of software portability, pLinguaCore can export a P system definition to any convenient output format (currently XML and binary formats are available). This software is not a closed product, but it can be extended to accept new input or output formats and also new models or simulators. The term P–Lingua 2.0 refers to the software package consisting of the above mentioned library together with a user interface called pLinguaPlugin (more details can be found at http://www.p-lingua.org). Finally, in order to illustrate the software, this paper includes an application using pLinguaCore for describing and simulating ecosystems by means of P systems.

A uniform family of tissue P systems with cell division solving 3-COL in a linear time

Several examples of the efficiency of cell-like P systems regarding the solution of NP-complete problems in polynomial time can be found in the literature(obviously, trading space for time). Recently, different new models of tissue-like P systems have received much attention from the scientific community. In this paper we present a linear-time solution to an NP-complete problem from graph theory, the 3-coloring problem, and we discuss the suitability of tissue-like P systems as a framework to address the efficient solution to intractable problems.

A fast P system for finding a balanced 2-partition

Numerical problems are not very frequently addressed in the P systems literature. In this paper we present an effective solution to the 2-Partition problem via a family of deterministic P systems with active membranes using 2-division. The design of this solution is a sequel of several previous works on other problems, mainly on the Subset-Sum and the Knapsack problems. Several improvements are introduced and explained.

A P-Lingua Programming Environment for Membrane Computing

A new programming language for membrane computing, P-Lingua, is developed in this paper. This language is not designed for a specific simulator software. On the contrary, its purpose is to offer a general syntactic framework that could define a unified standard for membrane computing, covering a broad variety of models. At the present stage, P-Lingua can only handle P systems with active membranes, although the authors intend to extend it to other models in the near future. P-Lingua allows to write programs in a friendly way, as its syntax is very close to standard scientific notation, and parameterized expressions can be used as shorthand for sets of rules. There is a built-in compiler that parses these human-style programs and generates XML documents that can be given as input to simulation tools, while different plugins can be designed to produce specific adequate outputs for existing simulators. Furthermore, we present in this paper an integrated development environment that plays the role of an interface where P-Lingua programs can be written and compiled. We also present a simulator for the class of recognizer P systems with active membranes, and we illustrate it by following the writing, compiling and simulating processes with a family of P systems solving the SAT problem.

MeCoSim: A general purpose software tool for simulating biological phenomena by means of P systems

In recent years, the increasing importance of the computational systems biology is leading to an impressive growth of the knowledge of several real-life phenomena. In this framework, membrane computing is an emergent branch within natural computing that has been succesfully used to model biological phenomena. The study of these phenomena usually requires the execution of virtual experiments using mechanisms of simulation, implying the development of ad-hoc tools to simulate. However, the advance of the research is demanding general solutions to avoid the necessity of custom software developments for each matter of study, when there are some common problems to resolve. MeCoSim (Membrane Computing Simulator) is a first step in this direction providing the users a customizable application to generate custom simulators based on membrane computing by simply writing a configuration file.

A Linear-Time Solution to the Knapsack Problem Using P Systems with Active Membranes

Up to now, P systems dealing with numerical problems have been rarely considered in the literature. In this paper we present an effective solution to the Knapsack problem using a family of deterministic P systems with active membranes using 2-division. We show that the number of steps of any computation is of linear order, but polynomial time is required for pre-computing resources.

Solving Subset Sum in Linear Time by Using Tissue P Systems with Cell Division

Tissue P systems with cell division is a computing model in the framework of Membrane Computing based on intercellular communication and cooperation between neurons. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to NP -complete problems in polynomial time. In this paper we present a solution to the Subset Sum problem via a family of such devices. This is the first solution to a numerical NP -complete problem by using tissue P systems with cell division.

Available Membrane Computing Software

The simulation of a P system with current computers is a quite complex task. P systems are intrinsically nondeterministic computational devices and therefore their computation trees are difficult to store and handle with computers with one processor (or a bounded number of processors). Nevertheless, there exists a first generation of simulators which can be successfully used for pedagogical purposes and as assistant tools for researchers. This chapter summarizes some of these simulators, presenting the state of the art of the available software for simulating (different variants of) cell-like membrane systems.

An unsupervised learning algorithm for membrane computing

This paper focuses on the unsupervised learning problem within membrane computing, and proposes an innovative solution inspired by membrane computing techniques, the fuzzy membrane clustering algorithm. An evolution-communication P system with nested membrane structure is the core component of the algorithm. The feasible cluster centers are represented by means of objects, and three types of membranes are considered: evolution, local store, and global store. Based on the designed membrane structure and the inherent communication mechanism, a modified differential evolution mechanism is developed to evolve the objects in the system. Under the control of the evolution-communication mechanism of the P system, the proposed fuzzy clustering algorithm achieves good fuzzy partitioning for a data set. The proposed fuzzy clustering algorithm is compared to three recently-developed and two classical clustering algorithms for five artificial and five real-life data sets.

Computational efficiency of dissolution rules in membrane systems

Trading (in polynomial time) space for time in the framework of membrane systems is not sufficient to efficiently solve computationally hard problems. On the one hand, an exponential number of objects generated in polynomial time is not sufficient to solve NP-complete problems in polynomial time. On the other hand, when an exponential number of membranes is created and used as workspace, the situation is very different. Two operations in P systems (membrane division and membrane creation) capable of constructing an exponential number of membranes in linear time are studied in this paper. NP-complete problems can be solved in polynomial time using P systems with active membranes and with polarizations, but when electrical charges are not used, then dissolution rules turn out to be very important. We show that in the framework of P systems with active membranes but without polarizations and in the framework of P systems with membrane creation, dissolution rules play a crucial role from the computational efficiency point of view.