Abstract
The recurrent neural network approach to combinatorial optimization has during the last decade evolved into a competitive and versatile heuristic method that can be used on a wide range of problem types. In the state-of-the-art neural approach the discrete elementary decisions (not necessarily binary) are represented by continuous Potts mean-field neurons, interpolating between the available discrete states, with a dynamics based on iteration of a set of mean-field equations. Driven by annealing in an artificial temperature, they will converge into a candidate solution.
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© 1999 Springer-Verlag
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Söderberg, B. (1999). Optimization with neural networks. In: Clark, J.W., Lindenau, T., Ristig, M.L. (eds) Scientific Applications of Neural Nets. Lecture Notes in Physics, vol 522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0104284
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DOI: https://doi.org/10.1007/BFb0104284
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