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OCR Free is text recognition software that performs all your tedious retyping and recreating work at lightning speed into Word documents you can edit on your ... [524] K-H Liang, X Yao, and C Newton Dynamic Control of Adaptive Parameters in Evolutionary Programming In Proceedings f the Second Asia-Pacific Conference on Simulated Evolution and Learning, Lecture Notes in Computer Science, volume 1585, pages 42 49, 1998 [525] Y-C Liang, S Kultural-Konak, and AE Smith Meta Heuristics for the Orienteering Problem In Proceedings of the IEEE Congress on Evolutionary Computation, volume 1, pages 384 389, May 2002 [526] S-F Lim and S-B Ho Dynamic Creation of Hidden Units with Selective Pruning in Backpropagation In IEEE World Congress on Computational Intelligence, Proceedings of the International Joint Conference on Neural Networks, volume 3, pages 492 497, 1994 [527] L Lin Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching Machine Learning, 8:293 321, 1992 [528] Y-C Lin, K-S Hwang, and F-S Wang Plant Scheduling and Planning using Mixed-Integer Hybrid Di erential Evolution with Multiplier Updating In Proceedings of the IEEE Congress on Evolutionary Computation, volume 1, pages 593 600, 2000 [529] Y-C Lin, K-S Hwang, and F-S Wang Hybrid Di erential Evolution with Multiplier Updating Method for Nonlinear Constrained Optimization Problems In Proceedings of the IEEE Congress on Evolutionary Computation, volume 1, pages 872 877, 2002 [530] Y-C Lin, K-S Hwang, and F-S Wang A Mixed-Coding Scheme of Evolutionary Algorithms to Solve Mixed-Integer Nonlinear Programming Problems Computers & Mathematics with Applications, 47(8-9):237 246, 2004 [531] Y-C Lin, F-S Wang, and K-S Hwang A hybrid Method of Evolutionary Algorithms For Mixed-Integer Nonlinear Optimization Problems In Proceedings of the IEEE Congress on Evolutionary Computation, volume 3, 1999 [532] Y Liu, X Yao, Q Zhao, and T Higuchi Scaling Up Fast Evolutionary Programming with Cooperative Coevolution In Proceedings of the IEEE Congress on Evolutionary Computation, volume 2, pages 1101 1108, 2001 [533] MF M ller A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning Neural Networks, 6:525 533, 1993 [534] M L vberg Improving Particle Swarm Optimization by Hybridization of Stochastic Search Heuristics and Self-Organized Criticality Master s thesis, Department of Computer Science, University of Aarhus, Denmark, 2002 [535] M L vberg and T Krink Extending Particle Swarm Optimisers with SelfOrganized Criticality In Proceedings of the IEEE Congress on Evolutionary Computation, volume 2, pages 1588 1593, 2002 [536] M L vberg, TK Rasmussen, and T Krink Hybrid Particle Swarm Optimiser with Breeding and Subpopulations In Proceedings of the Genetic and Evolutionary Computation Conference, pages 469 476, 2001 [537] J Ludik and I Cloete Training Schedules for Improved Convergence In IEEE International Joint Conference on Neural Networks, volume 1, pages 561 564, 1993 [538] J Ludik and I Cloete Incremental Increased Complexity Training In European Symposium on Artificial Neural Networks, pages 161 165, 1994. epson ocr software for windows 10: Apr 17, 2019 · 7 Best Free OCR Software Apps to Convert Images Into Text. OCR Using Microsoft OneNote. Microsoft OneN ... ocr software for windows 10 64 bitRating 4.0 epson ocr software windows 10 OCR Software for seamless digital text manipulation - Windows Report
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Tags API ... the deviation of the noise is determined by a strategy parameter, ij Generally, the step size is calculated as (112) xij (t) = ( ij (t)) ij (t) where : R R is a function that scales the contribution of the noise, ij (t) Based on the characteristics of the scaling function, , EP algorithms can be grouped into three main categories of algorithms: non-adaptive EP, in which case ( ) = In other words, the deviations in step sizes remain static dynamic EP, where the deviations in step sizes change over time using some deterministic function, , usually a function of the tness of individuals self-adaptive EP, in which case deviations in step sizes change dynamically The best values for ij are learned in parallel with the decision variables, xij Since the deviations, ij , have an in uence on the behavior of individuals in the case of dynamic and self daptive EP, these deviations are referred to as strategy parameters Each individual has its own strategy parameters, in which case an individual is represented as the tuple, (113) i (t) = (xi (t), i (t)) While deviations are the most popular choice for strategy parameters, Fogel [265, 266], extended EP to use correlation coe cients between components of the individual as strategy parameters, similar to their use in evolution strategies (refer to 12) Strategy parameters are discussed in more detail in Section 113 As is the case with all EAs, EP follows a stochastic search process Stochasticity is introduced by computing step sizes as a function of noise, ij , sampled from some probability distribution The following distributions have been used for EP: Uniform: Noise is sampled from a uniform distribution [580] ij (t) U (xmin,j , xmax,j ) (114). can essentially ignore it The computer is much more likely to break Your job is secure Instead of using a shuffling technique, you can achieve the same result by choosing the pivot randomly instead of deterministically Take a random item in the array and swap it with the item in position l o w Take another random item and swap it with the item in position high Take a third random item and swap it with the item in the middle position Then continue s usual As before, degenerate partitions are always possible, but they now happen as a result of bad random numbers, not bad inputs Let us look at the differences between randomized and nonrandomized algorithms So far we have concentrated on nonrandomized algorithms When calculating their average running times, we assume that all inputs are equally likely This assumption does not hold, however, because nearly sorted input, for instance, occurs much more often than is statistically expected This situation can cause problems for some algorithms, such as quicksort But when we use a randomized algorithm, the particular input is no longer important The random numbers are important, and we get an expected running time, in which we average over all possible random numbers for any particular input Using quickselect with random pivots (or a shuffle preprocessing step) gives an O(N) expected time algorithm That is, for any input, including already sorted input, the running time is expected to be O(N), based on the statistics of random numbers On the one hand an expected time bound is somewhat stronger than an average-case time bound because the assumptions used to generate it are weaker (random numbers versus random input) but it is weaker than the corresponding worst-case time bound On the other hand, in many instances solutions that have good worst-case bounds frequently have extra overhead built in to assure that the worst case does not occur The O(N)worst-case algorithm for selection, for example, is a marvelous theoretical result but is not practical Randomized algorithms come in two basic forms The first, as already shown, always gives a correct answer but it could take a long time, depending on the luck of the random numbers The second type is what we examine in the remainder of this chapter Some randomized algorithms work in a fixed amount of time but randomly make mistakes (presumably with low probability) called false positives or false negatives This technique is commonly accepted in medicine False positives and false negatives for most tests are actually fairly common, and some tests have surprisingly high error rates Furthermore, for some tests the errors depend on the individual, not random numbers, so repeating the test is certain to produce another false result In randomized algorithms we can rerun the test on the same input using different random numbers If we run a randomized algorithm 10 times and get 10 positives-and if a single false positive is an unlikely occurrence. barcode images into a Microsoft Word document using C# . 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