Abstract
We apply evolutionary computations to the Hopfield's neural network model of associative memory. In the model, some of the appropriate configurations of synaptic weights give the network a function of associative memory. One of our goals is to obtain the distribution of these configurations in the synaptic weight space. In other words, our aim is to learn a geometry of a fitness landscape defined on the space. For the purpose, we use evolutionary walks to explore the fitness landscape in this paper.
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Imada, A., Araki, K. (1998). What does the landscape of a Hopfield associative memory look like?. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040816
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DOI: https://doi.org/10.1007/BFb0040816
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