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Earthquake classifying neural networks trained with random dynamic neighborhood PSOs

Published: 07 July 2007 Publication History

Abstract

This paper investigates the use of Random Dynamic Neighborhoods in Particle Swarm Optimization (PSO) for the purposeof training fixed-architecture neural networks to classify a real-world data set of seismological data.Instead of the ring or fully-connected neighborhoods that are typically used with PSOs, or even more complex graph structures, this work uses directed graphs that are randomly generated using size and uniform out-degree as parameters. Furthermore, the graphs are subjected to dynamism during the course of a run, thereby allowing for varying information exchange patterns. Neighborhood re-structuring is applied with a linearly decreasing probability at each iteration. Several experimental configurations are tested on a training portion of the data set, and are ranked according to their abilities to generalize over the entire set. Comparisons are performed with standard PSOs as well as several static non-random neighborhoods.

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Cited By

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  • (2023)Neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early WarningMachine Learning, Image Processing, Network Security and Data Sciences10.1007/978-981-19-5868-7_26(345-356)Online publication date: 1-Jan-2023
  • (2014)Implementation of real coded PSO algorithms using FPGA technology2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)10.1109/STA.2014.7086765(213-218)Online publication date: Dec-2014
  • (2009)Design of artificial neural networks using a modified particle swarm optimization algorithmProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704611(2363-2370)Online publication date: 14-Jun-2009
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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
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    Published: 07 July 2007

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    Author Tags

    1. dynamic neighborhoods
    2. earthquake classification
    3. neighborhood configurations
    4. neural networks
    5. particle swarm optimization

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2023)Neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early WarningMachine Learning, Image Processing, Network Security and Data Sciences10.1007/978-981-19-5868-7_26(345-356)Online publication date: 1-Jan-2023
    • (2014)Implementation of real coded PSO algorithms using FPGA technology2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)10.1109/STA.2014.7086765(213-218)Online publication date: Dec-2014
    • (2009)Design of artificial neural networks using a modified particle swarm optimization algorithmProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704611(2363-2370)Online publication date: 14-Jun-2009
    • (2009)Accelerating the performance of particle swarm optimization for embedded applicationsProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689903(2294-2300)Online publication date: 18-May-2009
    • (2009)Enhancing performance of PSO with automatic parameter tuning technique2009 IEEE Swarm Intelligence Symposium10.1109/SIS.2009.4937846(67-73)Online publication date: Mar-2009
    • (2009)Multi-swarm parallel PSO: Hardware implementation2009 IEEE Swarm Intelligence Symposium10.1109/SIS.2009.4937845(60-66)Online publication date: Mar-2009
    • (2009)Design of artificial neural networks using a modified Particle Swarm Optimization algorithm2009 International Joint Conference on Neural Networks10.1109/IJCNN.2009.5178918(938-945)Online publication date: Jun-2009
    • (2009)Implementing Quantum-Behaved Particle Swarm Optimization Algorithm in FPGA for Embedded Real-Time ApplicationsProceedings of the 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology10.1109/ICCIT.2009.21(886-890)Online publication date: 24-Nov-2009
    • (2009)Accelerating the performance of particle swarm optimization for embedded applications2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983226(2294-2300)Online publication date: May-2009
    • (2008)Hardware PSO for sensor network applications2008 IEEE Swarm Intelligence Symposium10.1109/SIS.2008.4668308(1-8)Online publication date: Sep-2008

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