The network topology is represented by an adjacency matrix. For each edge between two nodes in the network there is a Boolean value of ' 1' in the matrix, and a ' 0' entry represents the absence of connection between the corresponding nodes. The adjacency matrix can be modified in Genetic Algorithm as follows [27]. We initially derive the chromosome (vector of 1 × 484) by concatenating the rows of the adjacency matrix of the network into one binary string. As such, the network topology of 22 nodes is represented as a chromosome i.e. a binary string of length 484. The topology of the basic network N1 was changed randomly using mutation and crossover. The crossover points were randomly selected uniformly from the range of [1:484]. Generally, the mutation rate for any particular bit is less than 1% [27]. In our implementation the mutation operator was applied to each of the bit in a chromosome (after performing crossover) with a low probability value of 0.01. Once the process of selection of chromosome, recombination and mutation is finished, the population of next generation is evaluated [27].