Genetic algorithms (GAs) are being used extensively in optimization problems as an alternative to traditional heuristics. Although the results have been mixed, very limited research has been performed on the impact of various GA factors on performance. We explore the use of GAs for solving a network optimization problem, the degree-constrained minimum spanning tree problem. We also examine the impact of encoding, crossover, and mutation on the performance of the GA. A specialized repair heuristic is used to improve performance. An experimental design with 48 cells and ten data points in each cell is used to examine the impact of two encoding methods (Prufer and determinant encoding), three crossover methods (one-point, two-point, and uniform), two mutation methods (insert and exchange), and four networks of varying node sizes (20, 40, 60, 80). Two performance measures, solution quality and computation time, are used to evaluate performance. The results indicate that encoding has the greatest effect on solution quality, followed by mutation and crossover. Among the various options, the combination of determinant encoding, exchange mutation, and uniform crossover more often provides better results for solution quality than other combinations. For computation time, the combination of determinant encoding, exchange mutation, and one-point crossover provides better results.
