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Artificial Neural Nets and Genetic Algorithms pp 359–363Cite as

Using Embryology as an Alternative to Genetic Algorithms for Designing Artificial Neural Network Topologies

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Abstract

This paper considers the role of the embryological algorithm or embryology in defining artificial neural network architecture. Such an approach is based on the biology and growth of the embryonic nervous system and operates by ‘growing’ the neural network from a simple to a complex form. The operation of both the embryology and the genetic algorithm are considered, contrasted and compared. A practical algorithm is presented, together with results demonstrating the relevance, application and advantages of the algorithm.

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References

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Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Robert Gordon University, Aberdeen, UK

    C. MacLeod & G. Maxwell

Authors
  1. C. MacLeod
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  2. G. Maxwell
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© 1998 Springer-Verlag Wien

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MacLeod, C., Maxwell, G. (1998). Using Embryology as an Alternative to Genetic Algorithms for Designing Artificial Neural Network Topologies. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_79

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  • DOI: https://doi.org/10.1007/978-3-7091-6492-1_79

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83087-1

  • Online ISBN: 978-3-7091-6492-1

  • eBook Packages: Springer Book Archive

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