Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to
heuristically solve boolean satisfiability (SAT) problems are presented. Results
are presented for two-peak and false-peak SAT problems. Since SAT is NP-Complete,
any other NP-Complete problem can be transformed into an equivalent
SAT problem in polynomial time, and solved via either paradigm. This technique
is illustrated for Hamiltonian circuit (HC) problems.