论文信息 - Genetic Algorithms and the Analysis of Spatially Referenced Data

Genetic Algorithms and the Analysis of Spatially Referenced Data

This article describes an application of genetic algorithms to the analysis of spatially referenced data. A genetic algorithm is used to refine the specification of an hedonic regression model of spatially distributed residential property prices. The process of refinement concerns the search for good definitions of spatially defined variables. The fitness function for the genetic algorithm is provided by the coefficient of determination of the model. The regression results produced by the refined model are compared with those produced by a model containing a set of spatially defined variables based on information provided by an expert on local property prices.

[1] Michael C. Ferris,et al. A Genetic Algorithm for Database Query Optimization , 1991, ICGA.

[2] J. S. Dodgson,et al. Valuing residential properties with the hedonic method: A comparison with the results of professional valuations , 1990 .

[3] Lawrence Davis,et al. A Genetic Algorithm for Survivable Network Design , 1993, International Conference on Genetic Algorithms.

[4] Paul Cheshire,et al. British Planning Policy and Access to Housing: Some Empirical Estimates , 1989 .

[5] Nashat Mansour,et al. A Distributed Genetic Algorithm for Employee Staffing and Scheduling Problems , 1993, ICGA.

[6] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7] David Beasley,et al. An overview of genetic algorithms: Part 1 , 1993 .

[8] M. D. Kidwell,et al. Using Genetic Algorithms to Schedule Distributed Tasks on a Bus-Based System , 1993, International Conference on Genetic Algorithms.

[9] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10] John J. Grefenstette,et al. How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.