An effective genetic algorithm for network coding

Abstract

Abstract-The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm.

Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources

“…Instead of the absolute information flow on links, a chromosome in [10] records the relative information flow, that is, an integer k , whose meaning is a certain predefined combination of signals on incoming links of a node. For instance, Figure 10 shows an illustration of how to predefine k .…”

“…In this paper, the above problem of minimizing network coding resources is referred to as the network coding problem (NCP). To address this problem, researchers have already attempted many different methods such as minimal approaches [ 4 , 5 ], linear programming methods [ 6 ], and genetic algorithms (GAs) [ 2 , 7 – 10 ]. These methods were all reported to be effective to minimize network coding resources.…”

A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization

“…But, the third category of the EAs utilizes the relative information of the flows [35]. To be specific, each link is associated with a coefficient which represents how the information is combined according to the combination of flows from the upstream links.…”

“…We evaluate the overall performance of NCRM-ACO by comparing it with the eleven state-of-the-art EAs, including 5 BLS-based (BLSGA [10], QEA1 [27], QEA2 [26], PBIL [28] and cGA [30]), 2 BTS-based (BTSGA [10] and FA-ENCA [34]), 3 relative-encoding-based (RGA [35], SRHCGA [37] and CRO [38]), and 1 path-oriented (pEA [32]). The algorithms for performance comparison are listed as follows.…”

“…For BTSGA, we have p c =0.8 and p m = 0.012. For the rest of the algorithms, we adopt their best parameter settings [26], [27], [29], [30], [32], [34], [35], [37], [38] . For the fixed, random and real-world networks, the stopping criteria is either an optimal solution is obtained or the maximum number of generations is reached.…”