Detecting Symmetry in Cellular Automata Generated Patterns Using Swarm Intelligence

Javaheri Javid, M. A., Zimmer, R. and Majid al-Rifaie, M. (2014) Detecting Symmetry in Cellular Automata Generated Patterns Using Swarm Intelligence. In: Theory and Practice of Natural Computing. Lecture Notes in Computer Science (LNCS), 8890 . Springer International Publishing, Switzerland, pp. 83-94. ISBN 9783319137483

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Since the introduction of cellular automata in the late 1940’s they have been used to address various types of problems in computer science and other multidisciplinary fields. Their generative capabilities have been used for simulating and modelling various natural, physical and chemical phenomena. Besides these applications, the lattice grid of cellular automata has been providing a by-product interface to generate graphical patterns for digital art creation. One important aspect of cellular automata is symmetry, detecting of which is often a difficult task and computationally expensive. In this paper a swarm intelligence algorithm – Stochastic Diffusion Search – is proposed as a tool to identify axes of symmetry in the cellular automata generated patterns.

Item Type: Book Sections
Additional Information: Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014, Proceedings Department of Engineering and Applied Design
Uncontrolled Keywords: algorithms, amorphous computing, artificial immune systems, bioinformatics,cellular automata,chaos and dynamical systems based computing,cryptography,economics, evolutionary computing, fractal geometry, learning, membrane computing, nanocomputing, neural computing, optical computing, optimization, pattern recognition, programming, quantum computing and quantum information, swarm intelligence
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Areas > Department of Engineering, Computing and Design
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Depositing User: Mohammad Ali Javaheri Javid
Date Deposited: 26 Mar 2020 15:11
Last Modified: 22 Feb 2022 09:04

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