Abstract
Nowadays, it is possible to find out different viable architectures that implements P Systems in a distributed cluster of processors. These proposed architectures have reached a certain compromise between the massively parallelism character of the system and the evolution step times. They are based in the distribution of several membranes in each processor, the use of proxies to control the communication between membranes and mainly, the suitable distribution of the architecture in a balanced tree of processors. For a given P-system and K processors, there exists a great volume of possible distributions of membranes over these. The main disadvantage related with these architectures is focused in the selection of the distribution of membranes that minimizes the external communications between them and maximizes the parallelism grade. In this paper, we suggest the use of Self-Organizing Neural Networks (SONN) with growing capability to help in this selection process for a given P-system.
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References
Păun, G.: Computing with membranes. Journal of Computer and System Sciences, 61 (2000), and Turku Center for Computer Science-TUCS Report No 208 (1998)
Tejedor, A., Fernandez, L., Arroyo, F., Bravo, G.: An architecture for attacking the bottleneck communication in P Systems. In: Sugisaka, M., Tanaka, H. (eds.) Proceedings of the 12th Int. Symposium on Artificial Life and Robotics, Beppu, Oita, Japan, pp. 500–505 (2007)
Bravo, G., Fernández, L., Arroyo, F., et al.: A hierarchical architecture with parallel communication for implementing P-Systems. In: ITA 2007 Xth Joint International Scientific Events on Informatics, Varna, Bulgaria (2007)
Bravo, G., Fernández, L., Arroyo, F., et al.: Master-Slave Distributed Architecture for Membrane Systems Implementation. In: 8th WSEAS Int. Conf. on Evolutionary Computing (EC 2007), Vancouver, Canada (2007)
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 4359–4369 (1982)
Fritzke, B.: Growing Cell Structures – A Self-organizing network for Unsupervised and Supervised learning. Neural Networks 7(1), 1441–1460 (1994)
Fritzke, B.: A growing neural gas network learns topologies. Advances in neural information processing systems 7, 625–632 (1995)
Deng, D., Kasabov, N.: On-line pattern analysis by evolving self-organising maps. Neurocomputing 51, 87–103 (2003)
Delgado, S., Gonzalo, C., Martínez, E., Arquero, A.: Improvement of Self-Organizing Maps with Growing Capability for Goodness Evaluation of Multispectral Training Patterns. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 1, pp. 564–567 (2004)
Gutiérrez, A., Fernández, L., Arroyo, F., Alonso, S.: Hardware and Software Architecture for Implementing Membrane Systems: A Case of Study to Transition P Systems. In: Garzon, M.H., Yan, H. (eds.) DNA 2007. LNCS, vol. 4848, pp. 211–220. Springer, Heidelberg (2008)
Gutierrez, A., Fernández, L., Arroyo, F., Alonso, S.: Suitability of Using Microcontrollers in Implementing new P System Communications Architectures. In: AROB 2008, XIII International Symposium on Artificial Life and Robotics, Oita, JAPAN, January 31-February 2 (2008)
Delgado, S., Gonzalo, C., Martinez, E., Arquero, A.: Visualizing High-Dimensional Input Data with Growing Self-Organizing Maps. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 580–587. Springer, Heidelberg (2007)
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Gutiérrez, A., Delgado, S., Fernández, L. (2009). Suitability of Using Self-Organizing Neural Networks in Configuring P-System Communications Architectures. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_53
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DOI: https://doi.org/10.1007/978-3-642-03040-6_53
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