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Detecting symmetry in cellular automata generated patterns using swarm intelligence

Detecting symmetry in cellular automata generated patterns using swarm intelligence

Javaheri Javid, Mohammad Ali, Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 and Zimmer, Robert (2014) Detecting symmetry in cellular automata generated patterns using swarm intelligence. In: Theory and Practice of Natural Computing Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings. Lecture Notes in Computer Science book series (LNCS), 8890 . Springer, Cham, Switzerland, pp. 83-94. ISBN 978-3319137483 ISSN 0302-9743 (Print), 1611-3349 (Online) (doi:https://doi.org/10.1007/978-3-319-13749-0_8)

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Abstract

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: Conference Proceedings
Title of Proceedings: Theory and Practice of Natural Computing Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings
Uncontrolled Keywords: Cellular automata, swarm intelligence, symmetry, aesthetics.
Subjects: B Philosophy. Psychology. Religion > BH Aesthetics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Computer & Computational Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 21 Jul 2021 13:03
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/21018

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