Geographical optimization using evolutionary algorithms
Ningchuan Xiao, Marc P. Armstrong · 2003
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 geographic analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem to be used on others. The purpose of this paper is to describe a framework that unifies the design and implementation of EAs for different types of geographic optimization problems. The key element in this framework is a graph representation that can be used to formally define the spatial structure of a broad range of geographic problems. Based on this representation, spatial constraints (e.g., contiguity and adjacency) of optimization problems can be effectively maintained, and general principles of designing evolutionary algorithms for geographic optimization are identified. The framework is applied to an example political redistricting problem. 1