Francis George C. CABARLE, Henry N. ADORNA,

Improving GPU Simulations of Spiking Neural P Systems
pp. 5–20


Abstract. In this work we present further extensions and improvements of a Spiking Neural P system (for short, SNP systems) simulator on graphics processing units (for short, GPUs). Using previous results on representing SNP system computations using linear algebra, we analyze and implement a computation simulation algorithm on the GPU. A two-level parallelism is introduced for the computation simulations. We also present a set of benchmark SNP systems to stress test the simulation and show the increased performance obtained using GPUs over conventional CPUs. For a 16 neuron benchmark SNP system with 65536 nondeterministic rule selection choices, we report a 2.31 speedup of the GPU-based simulations over CPU-based simulations. Read the pdf













Stephen Raj JEBASINGH, Thamburaj ROBINSON, Atulya  K. NAGAR
Constructing Non-Periodic Tiling Patterns with P System
pp. 21–32


Abstract. Parametric Tile Pasting P System (PTPPS) is a new computational model, based on the structure and functioning of living cells, to generate tiling patterns, using simple rules of gluing tiles (pasting rule) at their edges and geometric operations on the tiles. In this paper, a variant of PTPPS, namely Tissue-like Parametric Tile Pasting P System, with new geometric operations, to generate non-periodic patterns is introduced. The computational complexity of the system and the power of the system with different geometric operation are also discussed. Read the pdf.













Yunyun NIU, Linqiang PAN, Mario J. PÉREZ-JIMÉNEZ
Solving Common Algorithmic Problem by Recognizer Tissue P Systems
pp. 33–49


Abstract. Common Algorithmic Problem is an optimization problem, which has the nice property that several other NP-complete problems can be reduced to it in linear time. In this work, we deal with its decision version in the framework of tissue P systems. A tissue P system with cell division is a computing model which has two types of rules: communication and division rules. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to computationally hard problems in polynomial time. We here present an effective solution to Common Algorithmic Decision Problem by using a family of recognizer tissue P systems with cell division. Furthermore, a formal verification of this solution is given. Read the pdf













Yuquan LI, Gexiang ZHANG, Jixiang CHENG, Xiangxiang ZENG,
A Modified Estimation of Distribution Algorithm for Digital Filter Design
pp. 50–62


Abstract. Estimation of Distribution Algorithms (EDAs) are a class of probabilistic model-building evolutionary algorithms, which are characterized by learning and sampling the probability distribution of the selected individuals. This paper proposes a modified EDA (mEDA) for digital filter design. mEDA uses a novel sampling method, called centro-individual sampling, and a fuzzy C-means clustering technique to improve its performance. Extensive experiments conducted on a set of benchmark functions show that mEDA outperforms seven algorithms reported in the literature, in terms of the quality of solutions. Four types of digital infinite impulse response (IIR) filters are designed by using mEDA and the results show that mEDA can obtain better filter performance than four state-of-the-art methods.Read the pdf













Ravie Chandren MUNIYANDI, Abdullah Mohd. ZIN
Modeling Hormone-induced Calcium Oscillations in Liver Cell with Membrane Computing
pp. 63–76


Abstract. The capability of membrane computing to deal with distributed and parallel computing models, allows it to characterize the structure and processes of biological systems. With this advantage, membrane computing provides an alternative modelling approach to conventional methods such as ordinary differential equations, primarily in preserving the discrete and nondeterministic behavior of biological reactions. This paper investigates the implementation of the framework for modelling and verification based on membrane computing with a biological process of hormone-induced calcium oscillations in liver cell. The biological requirements and properties of this process are formalized in membrane computing. The model of membrane computing is verified with the simulation strategy of Gillespie algorithm and the model checking approach of the Probabilistic Symbolic Model Checker. The results provided by the simulation and model checking approaches demonstrate that the fundamental properties of the biological process have been preserved by membrane computing model. The results have emphasized that membrane computing provides a better approach in accommodating the structure and processes of hormone-induced calcium oscillations compared to the approach of the ordinary differential equations. However other biological aspects such as the selection of parameters based on the stochastic behavior of biological processes have to be tackled to strengthen membrane computing competence in modelling biological processes. Read the pdf