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Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.



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Successful user-guided optimization calls for skill, domain knowledge, or both, on the part of the person guiding the optimization process. Given adequate skill and experience, not to mention a tractable problem, user-guided optimization can be extremely efficient and dramatically faster than brute force methods. Ibe speed and efficiency derive from the addition of intelligence to the search process: Zones with a high probability of paying off can be recognized and carefully examined, while time-consuming investigations of regions unlikely to yield good results can be avoided. User-guided optimization is most appropriate when ballpark results have already been established by other means, when the problem is familiar or well understood, or when only a small number of parameters need to be manipulated. As a means of polishing an existing solution, user guided-optimization is an excellent choice. It is also useful for studying model sensitivity to changes in rules or parameter values.

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With the arrival of low-cost, high-speed solid-state microelectronics in the 1970s, it became possible to take significant steps forward in switching technology One of the first innovative steps was packet switching In packet switching, the message that was transmitted in its entirety over the earlier message-switched store-and-forward networks is now broken into smaller, more manageable pieces that are numbered by the Transport Layer before being passed into the network for routing and delivery This innovation offers several advantages First, it eliminates the need for the mechanical, switch-based hard drive, because the small packets can now be handled blindingly fast by solid-state memory Second, should a packet arrive with errors, it and it alone can be discarded and replaced In message-switched environments, an unrecoverable bit error resulted in the inevitable retransmission of the entire message not a particularly elegant solution Packet switching, then, offers a number of distinct advantages.

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Imagine something powerful enough to solve all the problems inherent in the creation of a human being. That something surely represents the ultimate in problem solving and optimization. What is it It is the familiar process of evolution. Genetic optimizers endeavor to harness some of that incredible problem-solving power through a crude simulation of the evolutionary process. In terms of overall performance and the variety of problems that may be solved, there is no general-purpose optimizer more powerful than a properly crafted genetic one. Genetic optimizers are Stochustic optimizers in the sense that they take advantage of random chance in their operation. It may not seem believable that tossing dice can be a great way to solve problems, but, done correctly, it can be! In addition to randomness, genetic optimizers employ selection and recombination. The clever integration of random chance, selection, and recombination is responsible for the genetic optimizer s great power. A full discussion of genetic algorithms, which are the basis for genetic optimizers, appears in Part II. Genetic optimizers have many highly desirable characteristics. One such characteristic is speed, especially when faced with combinatorial explosion. A genetic optimizer can easily be many orders of magnitude faster than a brute force optimizer when there are a multiplicity of rules, or parameters that have many possible values, to manipulate. This is because, like user-guided optimization, genetic optimization can focus on important regions of solution space while mostly ignoring blind alleys. In contrast to user-guided optimization, the benefit of a selective search is achieved without the need for human intervention. Genetic optimizers can swiftly solve complex problems, and they are also more immune than other kinds of optimizers to the effects of local maxima in the

The Interlocked class can be extremely useful if you are performing atomic operations You don t need to do anything other than invoke InterlockedIncrement(ref number) if you want to add 1 to an integer variable without worrying about other threads getting in the way If you want to do something beyond a simple increment, add, or comparison, you need a mechanism to turn an arbitrary set of operations into an atomic operation that can t be affected by other threads This atomicity is achieved by using a synchronization mechanism A synchronization mechanism is a way for a thread to gain exclusive access to something (possibly one or more resources), locking out all other threads When a thread is done with its work, it sends a signal essentially saying I m done and letting other threads then obtain access to the shared resources.

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