Minimum-weight spanning tree algorithms a survey and empirical study
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
On the History of the Minimum Spanning Tree Problem
IEEE Annals of the History of Computing
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
A Plasmid Based Transgenetic Algorithm for the Biobjective Minimum Spanning Tree Problem
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
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Combinatorial optimization problems with multiple objectives are, in general, more realistic representations of practical situations than their counterparts with a single-objective. The bi-objective minimum spanning tree problem is a NP-hard problem with applications in network design. In this paper a memetic algorithm is presented to solve this problem. A computational experiment compares the proposed approach with AESSEA, a known algorithm of the literature. The comparison of the algorithms is done with basis on the binary additive (-indicator. The results show that the proposed algorithm consistently produces better solutions than the other method.