用基因表达式编程生成数字高程模型

数字高程模型是地理信息系统进行地形分析的基础。在处理采样点缺失时，常用的空间插值方法难于客观地确定插值函数结构和参数。本文将基因表达式编程技术应用到数字高程模型的生成中，做了下列工作：（1）形式化描述高程基因，缓冲区和簇等概念；（2）证明了深度定理及引理；（3）提出了缓冲区策略（BS），适时注入优质高程体；（4）提出了动态簇的搜索算法（DCSA），方向搜索相关点算法（SCPD）和象限扩展搜索算法（ESDA），按方向动态扩展搜索簇，避免了冗余点的计算；提出了移动拟合GEP算法（EF-GEP），插值空间细分，缩小了拟合空间；（5）实验表明新方法是有效的，平均进化代数为227代，平均最高适应度为97.4527，提高了7.23%。三维地质模型形象地表达出了地质体形态结构。

Generate Digital Elevation Model by Gene Expression Programming

Digital Elevation Model is the foundation to analyze terrain in Geographic Information System. When processing the lost sampled points, the traditional spatial interpolation methods are ineffective and impersonal in selection the interpolation function, structures as well as its parameters. This paper applies the technology of Gene Expression Programming to generation of Digital Elevation Model. The contributions include: (1)formalizing the concepts of elevation gene, buffer and cluster, etc.; (2)proving the Depth Theorem and its lemmas; (3)proposing BS strategy, timely introducing good terrain individuals; (4)proposing the algorithms of DCSA, SCPD and ESDA to dynamically search cluster by direction and avoid computing the redundant points; proposing EF-GEP algorithm. The interpolation space is subdivided and the fitting space is reduced. (5)the experiments show that the average generation is 227, the average top fitness is 97.4527 and increased by 7.23%; 3-D Geological model visually express the configuration of geologic body.