Solving constraint satisfaction problems using neural networks

Describes GENET, a generic neural network simulator, that can solve general CSPs with finite domains. GENET generates a sparsely connected network for a given CSP with constraints C specified as binary matrices, and simulates the network convergence procedure. In case the network falls into local minima, a heuristic learning rule is applied to escape from them. The network model lends itself to massively parallel processing. The experimental results of applying GENET to randomly generated, including very tight constrained, CSPs and the real life problem of car sequencing is reported and an analysis of the effectiveness of GENET given.<>