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Choose Insert New Symbol, choose the Button behavior, and name it frame Draw a rectangle and drag it so that it s centered over the registration point We made ours

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It is important to make sure that you are saving your project to a reasonably fast hard drive The built-in hard drive on your iMac usually will handle the data requirements just fine Be careful, however, of trying to work off of external storage disks or drives In some cases, slower external hard drives will cause data failures Once you have given a location and name to your project, iMovie will create a folder with that name Inside that folder will be an iMovie file with the project name and a folder called Media The iMovie file is what professionals would refer to as an Edit Decision List (EDL) It carries nothing more than a set of instructions for how iMovie is supposed to put your video together The pieces of your video actually sit in the Media folder

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[61] H-G Beyer Toward a Theory of Evolution Strategies Some Asymptotical , Results from The (1 + )-Theory Evolutionary Computation, 1(2):165 188, 1993 [62] H-G Beyer Toward a Theory of Evolution Strategies: On The Bene ts of Sex The( / , ) Theory Evolutionary Computation, 3(1):81 111, 1995 [63] H-G Beyer Toward a Theory of Evolution Strategies: The ( , )-Theory Evolutionary Computation, 2(4):381 407, 1995 [64] H-G Beyer Toward a Theory of Evolution Strategies: Self-Adaptation Evolutionary Computation, 3(3):311 347, 1996 [65] H-G Beyer Mutate Large, but Inherit Small! On the Analysis of Rescaled Mutations in (1, )-ES with Noisy Fitness Data In AE Eiben, T B ck, M Schoea nauer, and H-P Schwefel, editors, Proceedings of the Parallel Problem Solving from Nature Conference, pages 109 118 Springer Verlag, 1998 [66] Z Bian, Y Yu, B Zheng, M Wang, and H Mao A Novel Evolution Strategy Algorithm Based on the Selected Direction by The Polar Coordinates In International Symposium on Systems and Control in Aerospace and Astronautics, pages 907 911, 2006 [67] G Bilchev and IC Parmee The Ant Colony Metaphor for Searching Continuous Design Spaces In T Fogarty, editor, Proceedings of the AISB Workshop on Evolutionary Computation, Lecture Notes in Computer Science, volume 993, pages 25 39 Springer-Verlag, 1995 [68] G Bilchev and IC Parmee Constrained Optimisation with an Ant Colony Search Model In Proceedings of Adaptive Computing in Engineering Design and Control, pages 145 151, 1996 [69] HK Birru Empirical Study of Two Classes of Bit Variation Operators in Evolutionary Computation In Proceedings of the IEEE Congress on Evolutionary Computation, volume 3, 1999 [70] HK Birru, K Chellapilla, and SS Rao Local Search Operators in Fast Evolutionary Programming In Proceedings of the IEEE Congress on Evolutionary Computation, volume 2, 1999 [71] C Bishop Exact Calculation of the Hessian Matrix for the Multilayer Perceptron Neural Computation, 4:494 501, 1992 [72] TM Blackwell Particle Swarms and Population Diversity II: Experiments In Genetic and Evolutionary Computation Conference, Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pages 14 18, 2003 [73] TM Blackwell Swarms in Dynamic Environments In Proceedings of the Genetic and Evolutionary Computation Conference, Lecture Notes in Computer Science, volume 2723, pages 1 12, 2003 [74] TM Blackwell and PJ Bentley Don t Push Me! Collision-Avoiding Swarms In Proceedings of the IEEE Congress on Evolutionary Computation, volume 2, pages 1691 1696, 2002 [75] TM Blackwell and PJ Bentley Dynamic Search with Charged Swarms In Proceedings of the Genetic and Evolutionary Computation Conference, pages 19 26, 2002.

Di erential evolution has mostly been applied to optimize functions de ned over continuous-valued landscapes [695, 811, 813, 876] Considering an unconstrained optimization problem, such as listed in Section A53, each individual, xi , will be represented by an nx -dimensional vector where each xij R For the initial population, each individual is initialized using xij U (xmin,j , xmax,j ) The tness function is simply the function to be optimized DE has also been applied to train neural networks (NN) (refer to Table 131 for references) In this case an individual represents a complete NN Each element of an individual is one of the weights or biases of the NN, and the tness function is, for example, the sum-squared error (SSE) Table 131 summarizes a number of real-world applications of DE Please note that this is not meant to be a complete list (1336)

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