Abstract
Feed-forward neural network structures, trained with back-propagation, are adapted by the use of the genetic algorithm (GA). Through this search technique problem specific topologies are found. The method used to represent network structure, in a form suitable for the GA, is investigated. Comparisons are made between three such encoding methods. Details are given of how these representational schemes can influence the performance of both the genetic algorithm and the resultant neural networks found by the GA.
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Roberts, S.G., Turega, M. (1995). Evolving Neural Network Structures: An Evaluation of Encoding Techniques. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_27
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_27
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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