Accessibility navigation

A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems

Davis-Moradkhan, M. and Browne, W.N. (2008) A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems. In: 8th International Conference on Hybrid Intelligent Systems (HIS 2008) , Barcelona, Spain,

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/HIS.2008.86


A hybridised and Knowledge-based Evolutionary Algorithm (KEA) is applied to the multi-criterion minimum spanning tree problems. Hybridisation is used across its three phases. In the first phase a deterministic single objective optimization algorithm finds the extreme points of the Pareto front. In the second phase a K-best approach finds the first neighbours of the extreme points, which serve as an elitist parent population to an evolutionary algorithm in the third phase. A knowledge-based mutation operator is applied in each generation to reproduce individuals that are at least as good as the unique parent. The advantages of KEA over previous algorithms include its speed (making it applicable to large real-world problems), its scalability to more than two criteria, and its ability to find both the supported and unsupported optimal solutions.

Item Type:Conference or Workshop Item (Paper)
ID Code:14641
Uncontrolled Keywords:Pareto optimisation, evolutionary computation, knowledge based systems, trees (mathematics) , deterministic single objective optimization algorithm, hybridised evolutionary algorithm, knowledge-based evolutionary algorithm, knowledge-based mutation operator, multicriterion minimum spanning tree problems , Evolutionary Algorithm, Multi-Criterion Minimum Spanning Tree

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation