Continuous-space embedding genetic algorithm applied to the degree constrained minimum spanning tree problem

Authors:
Tiago L. Pereira;Eduardo G. Carrano;Ricardo H. C. Takahashi;Elizabeth F. Wanner;Oriane M. Neto
Affiliations:
Department of Electrical Engineering, Universidade Federal de Minas Gerais, Brazil;Centro Federal de Educação Tecnológica de Minas Gerais, Brazil;Department of Mathematics, Universidade Federal de Minas Gerais, Brazil;Department of Mathematics, Universidade Federal de Ouro Preto, Brazil;Department of Electrical Engineering, Universidade Federal de Minas Gerais, Brazil
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 6
Cited 1

Comparison of Algorithms for the Degree Constrained Minimum Spanning Tree

Journal of Heuristics
Representations for Genetic and Evolutionary Algorithms

Representations for Genetic and Evolutionary Algorithms
Nonlinear network optimization: an embedding vector space approach

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Genetic algorithms for communications network design - an empiricalstudy of the factors that influence performance

IEEE Transactions on Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees

IEEE Transactions on Evolutionary Computation
From the Dandelion Code to the Rainbow code: a class of bijective spanning tree representations with linear complexity and bounded locality

IEEE Transactions on Evolutionary Computation

Degree constrained minimum spanning tree problem: a learning automata approach

The Journal of Supercomputing

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Abstract

This work presents an evolutionary approach for solving a difficult problem of combinatorial optimization, the DCMST (Degree-Constrained Minimum Spanning Tree Problem). Three genetic algorithms which embed candidate solutions in the continuous space [1] are proposed here for solving the DCMST. The results achieved by these three algorithms have been compared with four other existing algorithms according to three merit criteria: i) quality of the best solution found; ii) computational effort spent by the algorithm, and; iii) convergence tendency of the population. The three proposed algorithms have provided better results for both solution quality and population convergence, with reasonable computational cost, in tests performed for 25-node and 50-node test instances. The results suggest that the proposed algorithms are well suited for dealing with the problem under study.