Abstract

ABSTRACT As one of the advanced analysis capabilities in GIS, network analysis provides strong decision support for users in searching optimal path, finding the nearest facility and determining the service area. To lead to an effective solution for GIS network analysis, a new random searching method --genetic algorithm is introduced and applied in this article. The classical combinatorial optimization problem (knapsack problem) is used to introduce the concepts of genetic algorithm and describe its various operations in detail, such as encoding, crossover, mutation and inversion. Selection of parameters to reach the optimal performance for the genetic algorithm is also discussed in general. The GIS network analysis is then formalized as a general combinatorial optimization problem consisting of an objective function and a general constraint condition. The implementation details of this algorithm are discussed in terms of encoding and genetic operations. Test results for a network with eighty nodes are presented to demonstrate the efficiency of the genetic algorithm and its application potential.