Multicast routing for multimedia communication
IEEE/ACM Transactions on Networking (TON)
A QoS Routing Method for Ad-Hoc Networks Based on Genetic Algorithm
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
A Multiobjective Model for QoS Multicast Routing Based on Genetic Algorithm
ICCNMC '03 Proceedings of the 2003 International Conference on Computer Networks and Mobile Computing
A Degree-Delay-Constrained Genetic Algorithm for Multicast Routing Tree
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
A genetic algorithm for the multiple destination routing problems
IEEE Transactions on Evolutionary Computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
QoS routing based on genetic algorithm
Computer Communications
GA-based heuristic algorithms for bandwidth-delay-constrained least-cost multicast routing
Computer Communications
Group multicast routing problem: A genetic algorithms based approach
Computer Networks: The International Journal of Computer and Telecommunications Networking
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A two-stage genetic algorithm for the multi-multicast routing
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks
Engineering Applications of Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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This paper presents a genetic-inspired multicast routing algorithm with Quality of Service (i.e., bandwidth and end-to-end delay) constraints. The aim is to efficiently discover a minimum-cost multicast tree (a set of paths) that satisfactorily helps various services from a designated source to multiple destinations. To achieve this goal, state of the art genetic-based optimization techniques are employed. Each chromosome is represented as a tree structure of Genetic Programming. A fitness function that returns a tree cost has been suggested. New variation operators (i.e., crossover and mutation) are designed in this regard. Crossover exchanges partial chromosomes (i.e., sub-trees) in a positionally independent manner. Mutation introduces (in part) a new sub-tree with low probability. Moreover, all the infeasible chromosomes are treated with a simple repair function. The synergy achieved by combing new ingredients (i.e., representation, crossover, and mutation) offers an effective search capability that results in improved quality of solution and enhanced rate of convergence. Experimental results show that the proposed GA achieves minimal spanning tree, fast convergence speed, and high reliability. Further, its performance is better than that of a comparative reference.