Genetic Algorithm Solution of Network Coding Optimization

Abstract

After the best optimizing approach of network coding is being studied, some methods are proposedbased on the characteristics of the network coding overhead optimization problem. First, two modifications areadded to the preprocessing phase: 1) How to generate a fitness value to a network coding scheme under a certainnetwork coding optimization request is presented. This makes different network coding optimization problems besolved with the same genetic algorithm. 2) An additional exam processing of the multi-in outgoing links is importedto reduce the solution space. Second, experimental results show that the random generated solution of networkcoding optimization problem can hardly achieve the multicast rate, three new steps are suggested be taken with thecommon genetic algorithm: 1) use more delicate member generating function to generate initial members; 2) addnew members at the beginning of each round of the genetic algorithm to avoid localized optimization; 3) assign afitness value based on each receiver’s data rate rather than ?1 to those network coding solutions which cannoachieve the max multicast rate. Experimental results show dramatic improvements in terms of both efficiency andresult.