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GPU Accelerated Genetic Clustering

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Simulated Evolution and Learning (SEAL 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7673))

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Abstract

Genetic and evolutionary algorithms have been used to find clusters in data with success. Unfortunately, evolutionary clustering suffers from the high computational costs when it comes to fitness function evaluation. The GPU computing is a recent programming and development paradigm introducing high performance parallel computing to general audience. This study presents a design, implementation, and evaluation of a genetic algorithm for density based clustering for the nVidia CUDA platform.

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Author information

Authors and Affiliations

  1. Department of Computer Science, VŠB-Technical University of Ostrava, 17.listopadu 15/2172, 708 33, Ostrava-Poruba, Czech Republic

    Pavel Krömer, Jan Platoš & Václav Snášel

  2. IT4Innovations, 17.listopadu 15/2172, 708 33, Ostrava-Poruba, Czech Republic

    Pavel Krömer, Jan Platoš & Václav Snášel

Authors
  1. Pavel Krömer
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  2. Jan Platoš
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  3. Václav Snášel
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Le Quy Don Technical University, 100 Hoang Quoc Viet Street, Cau Giay District,, Hanoi, Vietnam

    Lam Thu Bui

  2. School of Computer Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798,, Singapore, Singapore

    Yew Soon Ong

  3. Faculty of Information Technology,, Hanoi University, km9 Nguyen Trai Road, Hanoi, Vietnam

    Nguyen Xuan Hoai

  4. Graduate School of Engineering, Department of Computer Science, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

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© 2012 Springer-Verlag Berlin Heidelberg

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Krömer, P., Platoš, J., Snášel, V. (2012). GPU Accelerated Genetic Clustering. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_41

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  • DOI: https://doi.org/10.1007/978-3-642-34859-4_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34858-7

  • Online ISBN: 978-3-642-34859-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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