Synopses & Reviews
Evolutionary models (e.g., genetic algorithms, artificial life), explored in other fields for the past two decades, are now emerging as an important new tool in GIS for a number of reasons. First, they are highly appropriate for modeling geographic phenomena. Secondly, geographical problems are often spatially separate (broken down into local or regional problems) and evolutionary algorithms can exploit this structure. Finally, the ability to store, manipulate, and visualize spatial data has increased to the point that space-time-attribute databases can be easily handled.
Clearly written and cutting edge, this book will appeal not only to GIS practitioners and researchers, but also to other professionals confronted by spatial problems. It may also serve as a primary textbook for college-level courses in GIS, advanced spatial modeling method and information engineering, or as a supplementary textbook for computer science courses.
Table of Contents
Foreword, Michael F. Goodchild
Part I. Evolutionary Algorithms: An Introduction
1. Concepts of Evolutionary Modeling and Evolutionary Algorithms
Part II. Spatial Evolutionary Modeling: Algorithms and Models
2. Modeling Spatial Phenomena
Part III. Spatial Evolutionary Algorithms: Applications
3. Beyond Data: Handling Spatial and Analytical Contexts with Genetics-Based Machine Learning, Catherine Dibble
4. A Genetic Algorithm to Design Optimal Patch Configurations Using Raster Data Structures, Christopher Brooks
5. Designing Genetic Algorithms to Solve GIS Problems, Steven Van Dijk, Dirk Thierens, and Mark De Berg
6. Evolutionary Modeling of Routes: The Case of Road Design, Angela Guimaraes Pereira
7. Airspace Sectoring by Evolutionary Computation, Daniel Delahaye