Abstract
This paper describes how through simple means a genetic search towards optimal neural network architectures can be improved, both in the convergence speed as in the quality of the final result. This result can be theoretically explained with the Baldwin effect, which is implemented here not just by the learning process of the network alone, but also by changing the network architecture as part of the learning procedure. This can be seen as a combination of two different techniques, both helping and improving on simple genetic search.
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Boers, E.J.W., Borst, M.V., Sprinkhuizen-Kuyper, I.G. (1995). Evolving Neural Networks Using the “Baldwin Effect”. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_87
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_87
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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