A knowledge-based evolution strategy for the multi-objective minimum spanning tree problemMoradkhan, M. D. and Browne, W. N. (2006) A knowledge-based evolution strategy for the multi-objective minimum spanning tree problem. In: 2006 IEEE Congress on Evolutionary Computation, Vols 1-6. IEEE Congress on Evolutionary Computation. IEEE, New York, pp. 1376-1383. ISBN 9780780394872 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/CEC.2006.1688471 Abstract/SummaryA fast Knowledge-based Evolution Strategy, KES, for the multi-objective minimum spanning tree, is presented. The proposed algorithm is validated, for the bi-objective case, with an exhaustive search for small problems (4-10 nodes), and compared with a deterministic algorithm, EPDA and NSGA-II for larger problems (up to 100 nodes) using benchmark hard instances. Experimental results show that KES finds the true Pareto fronts for small instances of the problem and calculates good approximation Pareto sets for larger instances tested. It is shown that the fronts calculated by YES are superior to NSGA-II fronts and almost as good as those established by EPDA. KES is designed to be scalable to multi-objective problems and fast due to its small complexity.
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