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aggregating all of the content in to a short listing or a summary A summary template is the Content Management Server (CMS) is fast becoming a vital content-management tool that next type of template that you'll likely create in CMS In contrast to a detail template, the helps administrators and developers handle the ever-increasing amount of content on their summary template is the basis for pages that don't have any content of their own The only Web sites However, an authoritative source of product information has been missing until purpose for pages based on a summary template is to ummarize the content stored in other now pages For example, if you look at the default page for the press release section in the BOTS Consulting Web site (shown in Server 2002: A Complete Guide is the first listing of explains Microsoft Content ManagementFigure 12-3), you'll notice that it is a simple book thatall of the press releases in unlock the power of CMS lists the date of the press release, a short title, how to effectivelythat section The summary Administrators and developers alike will learn how and a link to the Web servers' Again, this page doesn't extensibility by using CMS features and to enhance their release itself scalability, flexibility, andhave any content of its own; it simply aggregates content from other pages In this case, API to manage content on the Microsoft platform it's exposing the display name, the start date, and the URL properties. UCC-128 In VS .NET Using Barcode maker for ASP .Related: Generate QR Code C# Data, Generate QR Code .NET Data, Generate QR Code Word Data

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1, 2, ). Making Barcode In Java Using Barcode maker for Java . EAN13 In Visual C#.NET Using Barcode printer for .(Kohonen Grossberg learning [15, 22, 24]) The learning rate lrate is a positive optimization parameter normally programmed to decrease with successive steps, as in Section 6-9 he binary-selector output v in Figure 6-5 identifies the template vector (6-39) closest to the current input x To produce this vector for display (Figure 6-6) or further computations, we program.Related: QR Code Generating Excel Image, QR Code Generating .NET Image, ASP.NET QR Code Generating Size

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Figure 71 The concept of edge scheduling in VS NET Draw QR Code n VS NET Figure 71 The concept of edge scheduling QR Code ISO/IEC18004 In NET Using Barcode creator for .

graph and employing architecture-dependent routing routines, as opposed to MH Unfortunately, they do not elaborate on the edge scheduling technique and how it is integrated into classic scheduling BSA (bubble scheduling and allocation) is another contention aware algorithm that employs edge scheduling and the undirected topology graph (Kwok and Ahmad [114]) While DLS can be regarded as a dynamic list scheduling algorithm (Section 512), BSA s approach is quite different, in that it rst allocates all nodes to one processor Subsequently, nodes are migrated to adjacent processors if bene cial Next, the nodes on the adjacent processors are considered for migration and so on until all processors have been regarded The routing of the communication is done incrementally with each migration of a node BSA has two issues owing to its scheduling strategy, which are analyzed in Sinnen [171] First, edge scheduling is performed with almost no restrictions For example, an edge might be scheduled earlier on a link at the end of its route than on a link at the beginning of the route Obviously, this contradicts causality; hence, it is not a realistic view of the network traf c Effectively, this approach only captures contention at the last link of a route Furthermore, the incremental routing can lead to situations where communications are routed in a circle or even use a link twice Another task scheduling algorithm that features the scheduling of messages is proposed in Dhodhi et al [55] In this genetic algorithm based heuristic each interprocessor communication is scheduled on the connection between the two communicating processors The communication network is not modeled, which consequently implies the assumption of a fully connected system Virtual Processors Contention for (any type of) resources can also be addressed with the virtual processor concept This concept is typically found in the domain of real time system scheduling (Liu [127]) Each resource of such a system is modeled as a virtual processor For interprocessor communication this means that each communication link is a (virtual) communication processor Correspondingly, the task concept is also abstracted, for example, a communication is considered a job that can only be processed on the (virtual) communication processor While this approach is very general, it makes scheduling much more dif cult This is because scheduling has to deal with heterogeneous jobs and heterogeneous (virtual) processors, both in terms of functionality Task scheduling, as discussed in this text, has only to deal with one type of task Processor heterogeneity, as introduced in Section 63, deals with different processing speeds, not functionality Due to this conceptual complexity, scheduling heuristics for virtual processors usually have two phases, one for the mapping of the jobs onto the processors and one for the actual ordering/scheduling of the jobs (see also Section 52) This functional heterogeneity of the virtual processor concept is a crucial shortcoming, as many existing task scheduling algorithms cannot be employed On the other hand, edge scheduling can be integrated into task scheduling with little impact on the general scheduling techniques Algorithms designed for the classic model can be used almost without any modi cation as will be seen later Also, a closer inspection reveals that edge scheduling is in fact similar to the concept of virtual.

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or parallel) GAs are discussed in Section 954 .

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Standard GAs use populations where all individuals are of the same xed size For an nx -dimensional search space, a standard GA nds a solution through application of the evolutionary operators to the complete nx -dimensional individuals It may happen that good individuals are found, but some of the genes of a good individual are nonoptimal It may be di cult to nd optimal allele for such genes through application of crossover and mutation on the entire individual It may even happen that crossover looses optimized genes, or groups of optimized genes Goldberg et al [321, 323, 324] developed the messy GA (mGA), which nds solutions by evolving optimal building blocks and combining building blocks Here a building block refers to a group of genes In a messy GA individuals are of variable length, and speci ed by a list of position-value pairs The position speci es the gene index, and the value speci es the allele for that gene These pairs are referred to as messy genes As an example, if nx = 4, then the individual, ((1, 0)(3, 1), (4, 0)(1, 1)), represents the individual 0 10 The messy representation may result in individuals that are over-speci ed or underspeci ed The example above illustrates both cases The individual is over-speci ed because gene 1 occurs twice It is under-speci ed because gene 2 does not occur, and has no value assigned Fitness evaluation of messy individuals requires strategies to cope with such individuals For over-speci ed individuals, a rst-come- rst-served approach is followed where the rst speci ed value is assigned to the repeating gene For under-speci ed individuals, a missing gene s allele is obtained from a competitive template The competitive template is a locally optimal solution As an example, if 1101 is the template, the tness of 0 10 is evaluated as the tness of 0101 The objective of mGAs is to evolve optimal building blocks, and to incrementally combine optimized building blocks to form an optimal solution An mGA is implemented using two loops as shown in Algorithm 98 The inner loop consists of three steps: Initialization to create a population of building blocks of a speci ed length, nm Primordial, which aims to generate small, promising building blocks Juxtapositional, to combine building blocks The outer loop speci es the size of the building blocks to be considered, starting with the smallest size of one, and incrementally increasing the size until a maximum size is reached, or an acceptable solution is found The outer loop also sets the best solution obtained from the juxtaposition step as the competitive template for the ext iteration.

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