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Evolving Plastic Neural Networks for Online Learning: Review and Future Directions
Oliver J. Coleman and Alan D. Blair
School of Computer Science and Engineering, University of New South Wales, Sydney, Australiaocoleman@cse.unsw.edu.au
blair@cse.unsw.edu.au
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.
Keywords: plastic neural networks, evolution, online learning, meta-learning
LNAI 7691, p. 326 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012