Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Tabu search for generalized minimum spanning tree problem
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
An efficient algorithm for generalized minimum spanning tree problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A GRASP-based approach to the generalized minimum spanning tree problem
Expert Systems with Applications: An International Journal
An evolutionary algorithm with solution archive for the generalized minimum spanning tree problem
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
The generalized minimum spanning tree (GMST) problem occurs in telecommunications network planning, where a network of node clusters needs to be connected via a tree architecture using exactly one node per cluster. The problem is known to be NP-hard, and even finding a constant factor approximation algorithm is NP-hard. In this paper, we present two heuristic search approaches for the GMST problem: local search and a genetic algorithm. Our computational experiments show that these heuristics rapidly provide high-quality solutions for the GMST and outperform some previously suggested heuristics for the problem. In our computational tests on 211 test problems (including 169 problems from the TSPLIB set), our local-search heuristic found the optimal solution in 179 instances and our genetic-algorithm procedure found the optimal solution in 185 instances (out of the 211 instances, the optimal solution is known in 187 instances). Further, on each of the 19 unsolved instances from TSPLIB, both our local-search heuristic and genetic-algorithm procedure improved upon the best previously known solution.