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The UPC-A Code and the assignment of manufacturer ID numbers is controlled in the ... ASP . NET /Windows Forms/Reporting Services/Compact Framework ... algorithm, summarized in Algorithm 1617, a swarm of particles is randomly created, where each particle represents a single NN Each NN plays in a tournament against a group of randomly selected opponents, selected from a competition pool (usually consisting of all the current particles of the swarm and all personal best positions) After each NN has played against a group of opponents, it is assigned a score based on the number of wins, losses and draws achieved These scores are then used to determine personal best and neighborhood best solutions Weights are adjusted using the position and velocity updates of any PSO algorithm Algorithm 1617 PSO Coevolutionary Game Training Algorithm Create and randomly initialize a swarm of NNs; repeat Add each personal best position to the competition pool; Add each particle to the competition pool; for each particle (or NN) do Randomly select a group of opponents from the competition pool; for each opponent do Play a game (using game trees to determine next moves) against the opponents, playing as rst player; Record if game was won, lost or drawn; Play a game against same opponent, but as the second player; Record if game was won, lost or drawn; end Determine a score for each particle; Compute new personal best positions based on scores; end Compute neighbor best positions; Update particle velocities; Update particle positions; until stopping condition is true; Return global best particle as game-playing agent; The basic algorithm as given in Algorithm 1617 has een applied successfully to the zero-sum games of tick-tack-toe [283, 580], checkers [284], and bao [156] Franken and Engelbrecht also applied the approach to the non-zero-sum game, the iterated prisoner s dilemma [285] A variant of the approach, using two competing swarms has recently been used to train agents for a probabilistic version of tick-tac-toe [654]. Support .Related: EAN-8 Generator .NET WinForms , Generate UPC-A .NET , Print Data Matrix .NET WinForms In Visual Studio NET Using Barcode generator for VS Related: QR Code Creating NET , NET Code 39 Generator , Code 128 Generator NET. 9.10 EXAMPLES. Recognize Code39 In .NET Using Barcode Control . NET framework Control to generate, create Code 3/9 mage in .NET applications. EAN13 In VS .NET Using Barcode printer for VS .Related: .NET Winforms Barcode Generating , Crystal Barcode Generating , Barcode Generating ASP.NET VB Contact Us..Related: UPC-E Generation .NET WinForms , Word Interleaved 2 of 5 Generator , UPC-E Generation Excel The evaluation of evidence for exponentially distributed ata. Comput. Stat. Data Anal. 12(12): 5682 5693. 2007. 4. H. Almuallim and T. G. Dietterich. Learning with many irrelevant features. In 9th National Conference on Arti cial Intelligence. Cambridge: MIT Press, pp. 547 552. 1991. 5. J. J. Alpigini, J. F. Peters, J. Skowronek, and N. Zhong, eds. Rough Sets and Current Trends in Computing. Proceedings. 3rd International Conference, Malvern, PA, October 14 16, 2002. Lecture Notes in Computer Science 2475. Berlin: Springer. 2002. 6. K. K. Ang and C. Quek. Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach. IEEE Trans. Neural Net. 17(5): 1301 1315. 2006. 7. C. Apt , F. Damerau, and S. M. Weiss. Automated learning of decision rules for e text categorization. ACM Trans. Inf. Sys. 12(3): 233 251. 1994. 8. S. Asharaf and M. N. Murty. An adaptive rough fuzzy single pass algorithm for clustering large data sets. Pattern Recog. 36(12): 3015 3018. 2004. 9. S. Asharaf, S. K. Shevade, and N. M. Murty. Rough support vector clustering. Pattern Recog. 38(10): 1779 1783. 2005. 10. J. Atkinson-Abutridy, C. Mellish, and S. Aitken. Combining information extraction with genetic algorithms for text mining. IEEE Intelligent Systems 19(3): 22 30. 2004. 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