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Considering its first quantity in 1960, "Advances in Computing" has got down to current special insurance of techniques in undefined, software program, desktop thought, layout and functions. It has additionally supplied individuals with a medium within which they could learn their topics in higher intensity and breadth than that allowed by means of average magazine articles. quantity 37 comprises exact evaluate articles on automatics programming; electronic sign processing; neural networks for development attractiveness; computational heuristics; high-level synthesis of electronic circuits; concerns in dataflow computing; and sociological historical past of the neural community controversy.

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Advances in Computers, Vol. 37

In view that its first quantity in 1960, "Advances in Computing" has got down to current targeted insurance of suggestions in undefined, software program, computing device concept, layout and functions. It has additionally supplied individuals with a medium within which they could study their topics in higher intensity and breadth than that allowed through general magazine articles.

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And Prac. of Knowl. Disc. in Databases, Cavtat-Dubrovnik, Croatia, pp. 132–143. Vekaria, K. & Clack, C. (1998). Selective crossover in genetic algorithms: An empirical study, Proc. of PPSN, Amsterdam, The Netherlands, September 27-30, 1998, pp. 438–447. , Lam, W. & Leung, K. S. (1999). Using evolutionary programming and minimum description length principle for data mining of bayesian networks, IEEE Trans. on PAMI 21(2): 174–178. , Lee, S. Y. & Leung, K. S. (2002). , Proc. of the Genetic and Evol.

GA GA-SN GA-AM GA-HN GS GES K2-T K2-R MWST 250 3593 ± 47 3843 ± 58 3875 ± 32 9118 ± 269 9040 ± 1866 3112 ± 321 733 ± 9 3734 ± 61 10 ± 1 500 3659 ± 41 3877 ± 44 4005 ± 43 9179 ± 285 9503 ± 1555 2762 ± 166 855 ± 25 4368 ± 152 10 ± 2 1000 3871 ± 53 4051 ± 59 4481 ± 46 9026 ± 236 12283 ± 1403 4055 ± 3, 4 1011 ± 14 5019 ± 67 11 ± 1 2000 4088 ± 180 4332 ± 78 4834 ± 52 9214 ± 244 16216 ± 2192 5759 ± 420 1184 ± 8 5982 ± 43 12 ± 1 Table 5. Averaged computing times (in seconds) and standard deviations (ALARM).

These methods are compared to our four evolutionary algorithms learning: the simple genetic algorithm (GA); genetic algorithm combined with a strategy of sequential niching (GA-SN); the hybrid sequential-spatial genetic approach (GA-HN); the genetic algorithm with the dynamic adaptive mutation scheme GA-AM. , 1989) consisting of 37 variables and 46 arcs. We use each of these networks to summarize: • Four training data sets for each network, each one containing a number of databases of the same size (250, 500, 1000 & 2000 samples).

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