Abstract
We present a hierarchically structured system for the evolution of connectionist systems. Our approach is exemplified by evolution paradigms for neural network topologies and weights. Our descriptions of a network’s connectivity are based on context-free grammars which are used to characterize signal flow from input to output neurons. Evolution of a simple control task gives a first impression about the capabilities of this approach.
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© 1993 Springer-Verlag/Wien
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Jacob, C., Rehder, J. (1993). Evolution of neural net architectures by a hierarchical grammar-based genetic system. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_12
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DOI: https://doi.org/10.1007/978-3-7091-7533-0_12
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82459-7
Online ISBN: 978-3-7091-7533-0
eBook Packages: Springer Book Archive