| Sumario: | During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem from being used on others. In this article, a formal, conceptual framework is developed to unify the design and implementation of EAs for many geographical optimization problems. The key element in this framework is a graph representation that defines the spatial structure of a broad range of geographical problems. Based on this representation, four types of geographical optimization problems are discussed and a set of algorithms is developed for problems in each type. These algorithms can be used to support the design and implementation of EAs for geographical optimization. Knowledge specific to geographical optimization problems can also be incorporated into the framework. An example of solving political redistricting problems is used to demonstrate the application of this framework. Reprinted by permission of the publisher.
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