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A Probabilistic Solution Generator of Good Enough Designs for Simulation

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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


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