Solution of any search problem lies in its search space. A search is a systematic examination of candidate solutions of a search problem. In this thesis, we present a search heuristic that we can cr-smodels. cr-smodels prunes the search space to quickly reach to the solution of a problem. The idea is to pick an atom for branching , that lowers the growth rate of the linear recurrence and thuse, minimizes the remaining search space. Our goal in developing cr-smodels is to develop a search heuristic that is efficient on a wide range of problems. Then, we test cr-smodels over a wide range of randomly generated benchmarks. we observed that often randomly generated graphs with no Hamiltonian cycle were trivial to solve. Since, Hamiltonian cycle is an important benchmark problem, my other goal is to develop techniques that generate hard instances of graphs with no Hamiltonian cycle.
| Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_theses-1225 |
| Date | 01 January 2003 |
| Creators | Singhi, Soumya |
| Publisher | UKnowledge |
| Source Sets | University of Kentucky |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of Kentucky Master's Theses |
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