DocumentCode :
2850371
Title :
A Hybridised Evolutionary Algorithm for Multi-Criterion Minimum Spanning Tree Problems
Author :
Davis-Moradkhan, Madeleine ; Browne, Will N.
Author_Institution :
Sch. of Syst. Eng., Reading Univ., Reading
fYear :
2008
fDate :
10-12 Sept. 2008
Firstpage :
290
Lastpage :
295
Abstract :
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.
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 computation; Genetic mutations; Hybrid intelligent systems; Knowledge engineering; Lagrangian functions; Operations research; Pareto optimization; Polynomials; Scalability; Systems engineering and theory; Evolutionary Algorithm; Multi-Criterion Minimum Spanning Tree;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-0-7695-3326-1
Electronic_ISBN :
978-0-7695-3326-1
Type :
conf
DOI :
10.1109/HIS.2008.86
Filename :
4626644
Link To Document :
بازگشت