Neural optimization
Carsten Peterson, Bo Söderberg · 1998
Introduction Many combinatorial optimization problems require a more or less exhaustive search to achieve exact solutions, with the computational effort growing exponentially or worse with system size. Various kinds of heuristic methods are therefore often used to find reasonably good solutions. The artificial neural network (ANN) approach falls within this category. In contrast to most other methods, the ANN approach does not fully or partly explore the discrete state-space. Rather, it "feels" its way in a fuzzy manner through an interpolating, continuous space towards good solutions, and allows for a probabilistic interpretation. Key elements in this approach are the mean-field (MF) approximation (Hopfield and Tank, 1985; Peterson and S¨oderberg, 1989), annealing, and for many problems the Potts formulation (Peterson and S¨oderberg, 1989). Recently, also propagator methods have proven most valuable for handling