Author Description

Simulated annealing is a solution method in the field of combinatorial optimization based on an analogy with the physical process of annealing. The method is generally applicable, and can obtain solutions arbitrarily close to an optimum. However, finding high quality solutions can require large computational effort. The computational effort required can be greatly reduced by using the computational model of the Boltzmann machine. This is a neural network model which belongs to the class of connectionist models. It is characterized by massive parallelism and distributed representations. These features lead to a conceptually simple yet powerful model, which can be seen as an architectural blueprint for future parallel computers which can cope with higher order optimization problems such as learning. This book brings together in one volume the theory of simulated annealing and the model of the Boltzmann machine. It combines a mathematical treatment with a clear view of the applications which are already possible and the exciting developments which are beginning. It will be of great interest to graduate students and researchers in combinatorial optimization, numerical optimization, parallel processing, neural networks, computer science, artificial intelligence and automaton theory.