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Evolving Plastic Neural Networks for Online Learning: Review and Future Directions

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AI 2012: Advances in Artificial Intelligence (AI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7691))

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Abstract

Recent years have seen a resurgence of interest in evolving plastic neural networks for online learning. These approaches have an intrinsic appeal – since, to date, the only working example of general intelligence is the human brain, which has developed through evolution, and exhibits a great capacity to adapt to unfamiliar environments. In this paper we review prior work in this area – including problem domains and tasks, fitness functions, synaptic plasticity models and neural network encoding schemes. We conclude with a discussion of current findings and promising future directions, including incorporation of functional properties observed in biological neural networks which appear to play a role in learning processes, and addressing the “general” in general intelligence by the introduction of previously unseen tasks during the evolution process.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Oliver J. Coleman & Alan D. Blair

Authors
  1. Oliver J. Coleman
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  2. Alan D. Blair
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Michael Thielscher

  2. School of Computing and Mathematics, University of Western Sydney, 1797, Penrith South DC, NSW, Australia

    Dongmo Zhang

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© 2012 Springer-Verlag Berlin Heidelberg

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Coleman, O.J., Blair, A.D. (2012). Evolving Plastic Neural Networks for Online Learning: Review and Future Directions. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_28

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  • DOI: https://doi.org/10.1007/978-3-642-35101-3_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35100-6

  • Online ISBN: 978-3-642-35101-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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