Grant M. Maxwell

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This paper explains the optimisation of neuralnetwork topology using Incremental Evolution;that is, by allowing the network to expand byadding to its structure. This method allows anetwork to grow from a simple to a complexstructure until it is capable of fulfilling itsintended function. The approach is somewhatanalogous to the growth of an embryo or(More)
This paper outlines an algorithm for incrementally growing Artificial Neural Networks. The algorithm allows the network to expand by adding new sub-networks or modules to an existing structure; the modules are trained using an Evolutionary Algorithm. Only the latest module added to the network is trained, the previous structure remains fixed. The algorithm(More)
Connectionist approaches to artificial intelligence are almost always based on artificial neural networks. However, there is another route towards parallel distributed processing, taking as its inspiration the intelligence displayed by single celled creatures called protoctists (protists). This is based on networks of interacting proteins. Such networks may(More)
This paper shows how the process optimization methods known as Taguchi methods may be applied to the training of Artificial Neural Networks. A comparison is made between the efficiency of training using Taguchi methods and the efficiency of conventional training methods; attention is drawn to the advantages of Taguchi methods. Further, it is shown that(More)
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