Abstract
The goal of this paper is to present a more efficient way to automatically construct appropriate neural network topologies as well as their initial weight settings. Our approach combines evolutionary algorithms and genetic programming techniques and is based on a new network encoding schema where instead of a string like encoding the graph representation of neural nets is used. This way of “encoding” reduces the computational expense and leads to a greater variety of network topologies.
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© 1999 Springer-Verlag Berlin Heidelberg
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Golubski, W., Feuring, T. (1999). Evolving Neural Network Structures by Means of Genetic Programming. In: Poli, R., Nordin, P., Langdon, W.B., Fogarty, T.C. (eds) Genetic Programming. EuroGP 1999. Lecture Notes in Computer Science, vol 1598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48885-5_18
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DOI: https://doi.org/10.1007/3-540-48885-5_18
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