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International Conference on Intelligent Computing

ICIC 2017: Intelligent Computing Theories and Application pp 37–47Cite as

An Evolutionary Algorithm for Autonomous Agents with Spiking Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

Abstract

Inspired by the evolution of biological brains, the study of neurally-driven evolved autonomous agents has received more and more attention. In this paper, we propose an evolutionary algorithm for neurally-driven autonomous agents, each agent is controlled by a spiking neural network, and the network receives the sensory inputs and processes the motor outputs through the encoded spike information. The controlling spiking neural networks of autonomous agents are developed by the evolutionary algorithms that apply some of genetic operators and selection to a population of agents that undergo evolution. The corresponding food gathering experiment results show that the autonomous agents appear intelligent behaviours for the simulation environment. Additionally, the parameters of networks and agents play an important role in the evolutionary process.

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Acknowledgement

This research is supported by the Natural Science Foundation of Gansu Province of China under Grant No. 1506RJZA127, and the Scientific Research Project of Universities of Gansu Province under Grant No. 2015A-013.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Xianghong Lin, Fanqi Shen & Kun Liu

Authors
  1. Xianghong Lin
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  2. Fanqi Shen
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  3. Kun Liu
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Corresponding author

Correspondence to Xianghong Lin .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

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

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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© 2017 Springer International Publishing AG

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Lin, X., Shen, F., Liu, K. (2017). An Evolutionary Algorithm for Autonomous Agents with Spiking Neural Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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