By Theodore W Richards

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4. The flow diagram of SFFS In which, Y is features for all groups, D is the number of feature item, X is the best feature set selected from Y, R is the numbers of X, J(x) is the cost function that evaluate the classification accuracy of feature set X. The terminative condition of the SFFS algorithm is the number of features k. The SFFS algorithm is summarized as follows: Step 1: Input a feature group Y and set the terminative condition k. Step 2: Use the function J(x) to evaluated Y, that we can obtained a best feature x+, then put the x+ into the best feature group X.

1), which presents connection between two participants. The output of the E-model is a value that is closer to the quality of the call in modern networks, either narrowband or broadband. It is called the R-factor and it is in the range of 0 to 100. The R-factor is determined for the entire transmission chain. It takes into account not only the transmission channel, but also the end device. The higher the R-factor, the higher the quality of telephone service. The minimum acceptable value of R-factor is a ranking value 50.

The operations of the algorithm are, in contrast with some previous GAs for PMP, computationally inexpensive. The algorithm does not exploit any form of local search and does not use greedy steps in order to maintain generality. Therefore, it can be used for any other fixed-length subset selection problem when an appropriate fitness function is provided. New Genetic Algorithm for the p-Median Problem 4 39 New Genetic Algorithm for the p-Median Problem In order to design an efficient genetic algorithm for the PMP, we define the chromosome encoding, genetic operators, and fitness function.

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