Membrane Computing Applications in Computational Economics

Major efforts have been made along the last decade on the modelling and simulation of phenomena within areas such as Biochemistry, Ecology or Robotics, providing solutions for relevant problems (signalling pathways, population dynamics or logic gene networks, or robot control and planning, among others). However, other areas initially explored have not received the same amount of attention. This is the case of computational economics, where an initial model of the so-called producer-retailer problem was proposed by Gh. and R. P aun making use of membrane computing modelling and simulation tools. In the present paper, we start designing a solution for that problem based on PDP systems, obtaining results comparable with the foundational paper. Then, an enhanced and enriched model is proposed, including several economic issues not considered in the initial model as: depreciation of production capacity, capacity increase decision mechanism, dividends payment and costs associated to production factors. Additionally, both models have been simulated making use of the framework provided by P-Lingua and MeCoSim, and delivering a custom application based on them to reproduce the virtual experiments. Finally, several scenarios have been analysed focusing on different elements included in the model

From SAT to SAT-UNSAT using P systems with dissolution rules

DP is the class of problems that are the differences between two languages from NP. Most difficult problems from DP are called DP-complete problems, that can be seen as the conjunction of an NP-complete problem and a co-NP-complete problem. It is easy to see that the problem P vs NP is equivalent to the problem P vs DP, and therefore DP-complete problems would be better candidates to attack the conjecture, since they seem to be harder than NP-complete problems. In this paper, a methodology to transform an efficient solution of an NP-complete problem into an efficient solution of a DP-complete problem is applied. More precisely, a solution to SAT is given by means of a uniform family of recognizer polarizationless P systems with active membranes with dissolution rules and division rules for both elementary and non-elementary membranes, and later it is transformed into a solution to the problem SAT-UNSAT.Ministerio de Ciencia e Innovación TIN2017-89842-

The Computational Complexity of Tissue P Systems with Evolutional Symport/Antiport Rules

Tissue P systems with evolutional communication (symport/antiport) rules are computational models inspired by biochemical systems consisting of multiple individuals living and cooperating in a certain environment, where objects can be modified when moving from one region to another region. In this work, cell separation, inspired from membrane fission process, is introduced in the framework of tissue P systems with evolutional communication rules.The computational complexity of this kind of P systems is investigated. It is proved that only problems in class P can be efficiently solved by tissue P systems with cell separation with evolutional communication rules of length at most (��, 1), for each natural number �� ≥ 1. In the case where that length is upper bounded by (3, 2), a polynomial time solution to the SAT problem is provided, hence, assuming that P ̸= NP a new boundary between tractability and NP-hardness on the basis of the length of evolutional communication rules is provided. Finally, a new simulator for tissue P systems with evolutional communication rules is designed and is used to check the correctness of the solution to the SAT problem

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilisticmodels, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.Ministerio de Economía y Competitividad TIN2012-37434Junta de Andalucía P08-TIC-0420

A New P System to Model the Subalpine and Alpine Plant Communities

In this work we present a P system based model of the ecosystem dynamics of plant communities. It is applied to four National Hunting Reservoirs in Catalan Pyrenees (Spain). In previous works several natural high- mountain- ecosystems and population dynamics were modeled, but in those works grass was considered unlimited and changes in plant communities were not taken into account. In our new model we take advantage of the modularity of P systems, adding the plant communities to an existing model on scavengers dynamics. We introduce the plant community production and two possible changes or evolutions in communities: due to less grazing pressure, and due to recovering pastures with human management as for example re or clearing

Computing with viruses

In recent years, different computing models have emerged within the area of Unconven-tional Computation, and more specifically within Natural Computing, getting inspiration from mechanisms present in Nature. In this work, we incorporate concepts in virology and theoretical computer science to propose a novel computational model, called Virus Ma-chine. Inspired by the manner in which viruses transmit from one host to another, a virus machine is a computational paradigm represented as a heterogeneous network that con-sists of three subnetworks: virus transmission, instruction transfer, and instruction-channel control networks. Virus machines provide non-deterministic sequential devices. As num-ber computing devices, virus machines are proved to be computationally complete, that is, equivalent in power to Turing machines. Nevertheless, when some limitations are imposed with respect to the number of viruses present in the system, then a characterization for semi-linear sets is obtained

Computing Partial Recursive Functions by Virus Machines

Virus Machines are a computational paradigm inspired by the manner in which viruses replicate and transmit from one host cell to another. This paradigm provides non-deterministic sequential devices. Non-restricted Virus Machines are unbounded Virus Machines, in the sense that no restriction on the number of hosts, the number of instructions and the number of viruses contained in any host along any computation is placed on them. The computational completeness of these machines has been obtained by simulating register machines. In this paper, Virus Machines as function computing devices are considered. Then, the universality of non-restricted virus machines is proved by showing that they can compute all partial recursive functions.Ministerio de Economía y Competitividad TIN2012- 3743

Limits on P Systems with Proteins and Without Division

In the field of Membrane Computing, computational complexity theory has been widely studied trying to nd frontiers of efficiency by means of syntactic or semantical ingredients. The objective of this is to nd two kinds of systems, one non-efficient and another one, at least, presumably efficient, that is, that can solve NP-complete prob- lems in polynomial time, and adapt a solution of such a problem in the former. If it is possible, then P = NP. Several borderlines have been defi ned, and new characterizations of different types of membrane systems have been published. In this work, a certain type of P system, where proteins act as a supporting element for a rule to be red, is studied. In particular, while division rules, the abstraction of cellular mitosis is forbidden, only problems from class P can be solved, in contrast to the result obtained allowing them.Ministerio de Economía y Competitividad TIN2017-89842-PNational Natural Science Foundation of China No 6132010600

Narrowing Frontiers of Efficiency with Evolutional Communication Rules and Cell Separation

In the framework of Membrane Computing, several efficient solutions to computationally hard problems have been given. To find new borderlines between families of P systems that can solve them and the ones that cannot is an important way to tackle the P versus NP problem. Adding syntactic and/or semantic ingredients can mean passing from non-efficiency to presumably efficiency. Here, we try to get narrow frontiers, setting the stage to adapt efficient solutions from a family of P systems to another one. In order to do that, a solution to the SAT problem is given by means of a family of tissue P systems with evolutional symport/antiport rules and cell separation with the restriction that both the left-hand side and the right-hand side of the rules have at most two objects.Ministerio de Economía y Competitividad TIN2017-89842-PNational Natural Science Foundation of China No 6132010600

Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration

To reveal fault propagation paths is one of the most critical studies for the analysis of power system security; however, it is rather dif cult. This paper proposes a new framework for the fault propagation path modeling method of power systems based on membrane computing.We rst model the fault propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP system and make them suitable for large-scale power systems, we propose a model reduction method for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE 14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction, and the simulation results show a new success and promising approach to the engineering domain