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A New Learning Automata Algorithm for Selection of Optimal Subset

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

A new class of learning automata for the purpose of learning the optimal subset of actions has been proposed to fulfill the demand of application such as allocation and global optimization. Learning automata are capable of dealing with multiple choice problems if some modifications are made on current algorithms. This paper discusses on how to adapt current LA algorithms to the new purpose and introduces a new kind of learning automata. The proposed automata take advantage of LELA, whose original updating schemes favor the purpose of selecting multiple actions and thus acquire faster rate of convergence than the existing automata for selecting optimal subset of actions to the best of our knowledge. Additionally, extensive simulation results are presented to compare the performance between the proposed algorithm and the existing ones. The results show that the proposed automata outperform the other automata.

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Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xinyi Guo, Wen Jiang, Hao Ge & Shenghong Li

Authors
  1. Xinyi Guo
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  2. Wen Jiang
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  3. Hao Ge
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  4. Shenghong Li
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Corresponding author

Correspondence to Xinyi Guo .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2015 Springer International Publishing Switzerland

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Guo, X., Jiang, W., Ge, H., Li, S. (2015). A New Learning Automata Algorithm for Selection of Optimal Subset. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_69

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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