A Probabilistic Solution Generator of Good Enough Designs for Simulation
dc.contributor.author | Ozden, Mufit | en_US |
dc.contributor.author | Ho, Yu-Chi | en_US |
dc.date.accessioned | 2008-07-22T19:31:18Z | en_US |
dc.date.accessioned | 2013-07-10T15:06:41Z | |
dc.date.available | 2008-07-22T19:31:18Z | en_US |
dc.date.available | 2013-07-10T15:06:41Z | |
dc.date.issued | 1999-12-01 | en_US |
dc.date.submitted | 2007-11-26 | en_US |
dc.identifier.uri | ||
dc.identifier.uri | http://hdl.handle.net/2374.MIA/210 | en_US |
dc.description.abstract | We build a probabilistic solution generator using the learning automata theory, which can generate a small set of "good enough" designs with a predetermined high probability. The main goal of our work is to reduce a large design population to a much smaller subset of good designs that can be analyzed thoroughly in a subsequent simulation study to identify the best design among them. In the process of building the solution generator, a rough-cut design evaluation method with a high noise error is employed in order to screen designs very rapidly _ may it be an approximate method, a heuristic approach, or short simulation runs. The solution generator has been applied successfully to several serious test problems with noisy objectives. | en_US |
dc.subject | simulation | en_US |
dc.subject | stochastic optimization | en_US |
dc.subject | ordinal optimization | en_US |
dc.subject | learning automata theory | en_US |
dc.title | A Probabilistic Solution Generator of Good Enough Designs for Simulation | en_US |
dc.type | Text | en_US |
dc.type.genre | Article | en_US |