Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems
نویسنده
چکیده
USING NEURAL NETWORKS AND GENETIC ALGORITHMS AS HEURISTICS FOR NP-COMPLETE PROBLEMS William M. Spears, M.S. George Mason University, 1989 Thesis Director: Dr. Kenneth A. De Jong 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 NPComplete, 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.
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