Genetic algorithms for communications network design - an empirical study of the factors that influence performance
Genetic algorithms for communications network design - an empirical study of the factors that influence performance
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DOI：
10.1109/4235.930313
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发表时间：
2001-06
期刊：
影响因子：
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通讯作者：
Hsinghua Chou;G. Premkumar;Chao-Hsien Chu
中科院分区：
文献类型：
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作者：
Hsinghua Chou;G. Premkumar;Chao-Hsien Chu
我们探索使用 GA 来解决网络优化问题，即度约束的最小生成树问题。我们还研究了编码、交叉和变异对 GA 性能的影响。使用专门的修复启发式方法来提高性能。使用包含 48 个单元和每个单元中 10 个数据点的实验设计来检查两种编码方法、三种交叉方法、两种变异方法和四种不同节点大小的网络的影响。使用两个性能指标（解决方案质量和计算时间）来评估性能。所得结果表明编码对解质量的影响最大，其次是变异和交叉。在各种选项中，行列式编码、交换突变和均匀交叉的组合通常比其他组合提供更好的解决方案质量结果。对于计算时间，行列式编码、交换突变和单点交叉的组合提供了更好的结果。
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, three crossover methods, two mutation methods, and four networks of varying node sizes. Two performance measures, solution quality and computation time, are used to evaluate the performance. The results obtained 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.