An effective genetic algorithm for the network coding problem

The optimization of network coding is a relatively new area for evolutionary algorithms, as very few efforts have so far been reported. This paper is concerned with the design of an effective Genetic Algorithm (GA) for tackling the network coding problem (NCP). Differing from previous relevant works, the proposed GA is designed based on a permutation representation, which not only allows each chromosome to record a specific network protocol and coding scheme, but also makes it easy to integrate useful problem-specific heuristic rules into the algorithm. In the new GA, a more general fitness function is proposed, which, besides considering the minimization of network coding resources, also takes into account the maximization of the rate actually achieved. This new fitness function makes the proposed GA more suitable for the case of dynamic network coding, where any link could be cut off at any time, and consequently, the target rate might become unachievable even if all nodes allow coding. Based on the new representation and fitness function, other GA related techniques are modified and employed accordingly and carefully. Comparative experiments show that the proposed GA clearly outperforms previous methods