A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
A New Evolutionary Approach for the Optimal Communication Spanning Tree Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
On Optimal Solutions for the Optimal Communication Spanning Tree Problem
Operations Research
New insights into the OCST problem: integrating node degrees and their location in the graph
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A memetic algorithm for the optimum communication spanning tree problem
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
ICCSN '10 Proceedings of the 2010 Second International Conference on Communication Software and Networks
Making the edge-set encoding fly by controlling the bias of its crossover operator
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
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Optimal Communication Spanning Tree (OCST) is a well-known NP-hard problem on the graph that seeks for the spanning tree with the lowest cost. The tree cost depends on the demand and distance between each pair of nodes. This paper presents a Hybrid Genetic Algorithm (HGA) combining the basic GA with the ideas of the Particle Swarm Optimization (PSO) algorithm. In HGA, each individual exploits information of its own experience to search through the solution space with genetic operator. The experiment results show that our HGA outperforms the previous GAs with faster convergence and better solution.