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Evolutionary Design of Artificial Neural Networks

In: Soft Computing

Author

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  • Andrea Tettamanzi

    (University of Milan, Information Technology Department)

  • Marco Tomassini

    (University of Lausanne, Computer Science Institute)

Abstract

WE saw in Chapter 2 that artificial neural networks are biologically-inspired computational models that have the capability of somehow “learning” or “self-organizing” to accomplish a given task. They are particularly efficient when the nature of the task is ill-defined and the input/output mapping largely unknown. However, many aspects may affect the performance of an ANN on a given problem. Among them, the most important is the structure of the neuron connections i.e., the topology of the net, the connection weights, the details of the learning rules and of the neural activation function, and the data sets to be used for learning. There are guidelines for picking or finding reasonable values for all of these network parameters but most are rules of thumb with little theoretical background and without any relationship with each other.

Suggested Citation

  • Andrea Tettamanzi & Marco Tomassini, 2001. "Evolutionary Design of Artificial Neural Networks," Springer Books, in: Soft Computing, chapter 0, pages 123-159, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-04335-6_4
    DOI: 10.1007/978-3-662-04335-6_4
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