JAIR article: Connectionist Theory Refinement: Genetically..."

[Steve Minton]

Readers of this mailing list might be interested in the following article,
which was just published by JAIR:

Opitz, D.W. and Shavlik, J.W. (1997)
  "Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies",  Volume 6, pages 177-209.

   Available in HTML, Postscript (578K) and compressed Postscript (267K).
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     http://www.jair.org/abstracts/opitz97a.html
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   Abstract: An algorithm that learns from a set of examples should
   ideally be able to exploit the available resources of (a) abundant
   computing power and (b) domain-specific knowledge to improve its
   ability to generalize.  Connectionist theory-refinement systems, which
   use background knowledge to select a neural network's topology and
   initial weights, have proven to be effective at exploiting
   domain-specific knowledge; however, most do not exploit available
   computing power. This weakness occurs because they lack the ability to
   refine the topology of the neural networks they produce, thereby
   limiting generalization, especially when given impoverished domain
   theories. We present the REGENT algorithm which uses (a) domain-specific 
   knowledge to help create an initial population of knowledge-based
   neural networks and (b) genetic operators of crossover and mutation
   (specifically designed for knowledge-based networks) to continually
   search for better network topologies. Experiments on three real-world
   domains indicate that our new algorithm is able to significantly
   increase generalization compared to a standard connectionist
   theory-refinement system, as well as our previous algorithm for
   growing knowledge-based networks.

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