@inbook{986dcb1bffb24275b4e6de82172f542d,
title = "Optimization with Neural Networks",
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.",
author = "Bo S{\"o}derberg",
year = "1999",
language = "English",
series = "Lecture Notes in Physics",
publisher = "Springer",
pages = "243--256",
editor = "Clark, {J. W.} and T. Lindenau and Ristig, {M. L.}",
booktitle = "Scientific Applications of Neural Nets",
address = "Germany",
}