Comprehensive Study Of The Genetic Algorithm-based Mapping

On the basis of the analyzing the characteristics and difficulties of automated cartographic generalization, this article attempts to solve automated cartographic generalization by genetic algorithms (GA), which is a new intellectualized algorithms. According to the different propose and request, we think up several models of cartographic generalization based on genetic algorithms:1. Point Cluster Selection Based on Genetic AlgorithmsBased on analyzing the existing models of point cluster selection, considering of the range of distributing, the density of distributing, the reserved number, the importance of object and so on, combining the principles of genetic algorithms, we use the physical power to assimilate to the connection between points subtly, and put forward the model of point cluster selection based on genetic algorithms. The experimental output indicates that this model can conquer the shortcomings of exciting models and can keep general characteristics, get good results.2. Automated Line Simplification Based on Genetic AlgorithmsThis article points out that essential of line simplification is to get lest points and keep the precision. Combined the soul of the Douglas-peucker operator, we think out the model of line simplification based on genetic algorithms. It is easy to find that the model of line simplification based on genetic algorithms can get better output.3. Feature Labeling Based on Genetic AlgorithmsLabel is a very important part of Map Information. It is very important to study good labeling method. This article tries to apply genetic algorithms in feature labeling based on the viewpoint that labeling is a hard NP problem. Based on the keystone and method of genetic algorithms, combined with the based model of feature labeling, we discuss the method of point based on genetic algorithms and the keys to the method.4. Road Net Generalization Based on GABased on the structural information of road net, this article put the economical factors into considering. Combining with the basic framework of genetic algorithms, this article putout a model of road net generalization based on GA. The experimental output indicates this model is applied.