Abstract
Some discrete optimization problems can be programmed and solved on artificial neural networks. The NEURO-OPTIMIZER attains good (not necessarily optimal) solutions for general nonlinear discrete optimization problems through neurons state transitions. The simulated annealing method is introduced to escape from local minima. In this paper the number representation of the discrete variable by using neurons is investigated, and a mapping technique is proposed for irregularly discrete variables.
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© 1994 Springer-Verlag, Berlin Heidelberg
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Kishi, M., Kodera, T., Iwao, Y., Hosoda, R. (1994). Neuro-Optimizer, Its Application to Discrete Structural Optimization. In: Gutkowski, W., Bauer, J. (eds) Discrete Structural Optimization. International Union of Theoretical and Applied Mechanics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-85095-0_3
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DOI: https://doi.org/10.1007/978-3-642-85095-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-85097-4
Online ISBN: 978-3-642-85095-0
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