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The first version of adjacent_difference() creates a new sequence in which the value of each new element other than the first represents the difference of the current and previous element For example, given the sequence {0,1,1,2,3,5,8}, the first element of the new sequence is simply a copy of the first element of the original sequence: 0 The second element is the difference between the first two elements: 1 The third element is the difference between the second and third element, or 1 -1, or 0, and so on The new sequence is {0,1,0,1,1,2,3} The second version computes the adjacent difference using the specified binary operation For example, using the same sequence, let's pass in the times<int function object Again, the first element of the new sequence is simply a copy of the first element of the original sequence: 0 The second element is the product of the first and second element, also 0 The third element is the product of the second and third element, or 1 * 1, or 1, and so on The new sequence is {0,0,1,2,6,15,40} In both versions the OutputIterator points one past the last element of the new sequence adjacent_difference() is one of the numeric algorithms To use either version we must include the <numeric header file

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is de ned, which takes into account the ability of each parent to distinguish an individual from all others based on the most distinguishing features on the individual s face. This tness function also takes into account the similarity of the extracted features, and it discourages the selection of features that are highly correlated with each other. This ensures that the face images will be searched for multiple distinguishing characteristics. Section 5.6.3.1 explains in detail the tness function used in our experiments. The parents with the best tness are ranked higher and have the highest probability of being picked for using genetics the next generation. At the end of the rank ordering process, the parents are arranged in a descending order, based on their tness. This rank ordering determines the probability of each parent being used to create the subsequent generation. If a parent has a higher tness, it will have a higher probability of being cloned into the next generation, or of otherwise being involved in reproduction. 5.5.1.4 Creation of the Newer Generations

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#include <numeric> #include <list> #include <functional> #include <iterator> #include <iostreamh> int main()

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (1011 / 1065) [2001-3-29 11:32:15]

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user privacy protection is a concern. It is recommended that the identity of the client is communicated to the authenticator through method-specific signaling that provides confidentiality for messages carrying user identity. We will discuss EAP-TTLS as a prime example of such signaling. Some EAP methods also provide per-packet authentication, integrity and replay protection. The peer responds to the EAP request, with an EAP response message of the same type. For instance, if the request was an Identity request, the response would be an Identity response. If the type field in the EAP request had suggested an authentication method, the peer either responds to the needs of the authentication method with a EAP response message of the same type, or responds with an EAP response message of type NAK (type 3), indicating that the suggested authentication method is not acceptable. In such cases, the peer s response contains suggestions on one or more authentication methods. This is accomplished by including the type values for those methods within the type-data field of the NAK response message. We will talk about the authentication types in more detail shortly. From this point on many EAP request/response pairs may be exchanged between the peer and the authenticator to carry method-specific conversations. As mentioned before, after sending each EAP request, the authenticator (NAS) waits until it receives a response from the peer. The NAS does not have to understand each authentication method and can simply act as the pass-through agent for the EAP server. This will also facilitate upgrades of authentication methods as well as the credential management, since peer credentials do not have to be maintained (or cached ) at the NAS or even revealed to the NAS.

The newer generations are created from the older population using clones, mutants, and crossovers of the ttest parents. To better search for the optimal parameter set, new random parents are created every generation. This reduces the likelihood that the algorithm will get stuck in a local minimum in the search space. Figure 5.6 shows how crossover creates a newer generation, using the ttest parents from the older generation. The number of offsprings created from mutation, cloning, and crossover are determined by parameters of the genetic algorithm. The number of clones, mutants,

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