Parallel analog neural networks for tree searching
Janet Saylor, David G. Stork · AIP conference proceedings · 1986
We have modeled parallel analog neural networks designed such that their evolution toward final states is equivalent to finding optimal (or nearly optimal) paths through decision trees. This work extends that done on the Traveling Salesman Problem (TSP)[1] and sheds light on the conditions under which analog neural networks can and cannot find solutions to discrete optimization problems. Neural networks show considerable specificity in finding optimal solutions for tree searches; in the cases when a final state does represent a syntactically correct path, that path will be the best path 70–90% of the time—even for trees with up to two thousand nodes. However, it appears that except for trivial networks lacking the ability to ‘‘think globally,’’ there exists no general network architecture that can strictly insure the convergence a state that represents a single, continuous, unambiguous path. In fact, we find that for roughly 15% of trees with six generations, 40% of trees with eight generations, and 70% o...