A class of neural networks used for solving constrained optimization problems. In a typical Boltzmann machine , the weights are fixed to represent the constraints of the problem and the function to be optimized. the net seeks the solution by changing the activations ( either 1 or 0 ) of the units based on a probability distribution and the effect that the change would have on the energy function or consensus function for the net [Aarts 7 Korst, 1989] See also simulated annealing.