TR: Co-evolutionary Training of NNs

[Jan Paredis]

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The following paper is now available:

TITLE: Steps towards Co-evolutionary  Classification Neural Networks
AUTHOR: Jan Paredis
10 pages


To appear in: 
Proc. Artificial Life IV, R. Brooks, P. Maes (eds), 
MIT Press / Bradford Books.


ABSTRACT

This paper proposes two improvements to the genetic evolution of neural 
networks (NNs): life-time fitness evaluation and co-evolution. A classi-
fication task is used to demonstrate the potential of these methods and to 
compare them with state-of-the-art evolutionary NN approaches. 
Furthermore, both methods are complementary: co-evolution can be used 
in combination with life-time fitness evaluation.

Moreover, the continuous feedback associated with life-time evalua-
tion paves the way for the incorporation of life-time learning. This may lead 
to hybrid approaches which involve genetic as well as, for example, back-
propagation learning. In addition to this, life-time fitness evaluation allows 
an apt response to noise and changes in the problem to be solved.

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Jan Paredis
RIKS
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email: jan at riks.nl
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