Exploring the Solution Space of Semi-structured Geographical Problems Using Genetic Algorithms |
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| Authors: | David A Bennett Greg A Wade & Marc P Armstrong |
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| Institution: | Department of Geography, University of Kansas,;Lockheed Martin Mission Systems, O'Fallon, IL 62269,;Department of Geography &Program in Applied Mathematical &Computational Sciences, University of Iowa |
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| Abstract: | Semi-structured geographical problems are often addressed by groups of decision-makers. Each group member is likely to have a specific set of objectives that they wish to address and a unique perspective on the way in which the problem should be solved. The solution to such problems often requires consensus building and compromise among decision-makers as they attempt to optimize their own criteria. The set of criteria adopted by a particular decision-maker constrains the set of solutions he/she will deem acceptable. Compromise among multiple decision-makers can occur at the intersection of these constrained solution sets. Knowledge about the criteria space, the solution space, and the relation between the two is often incomplete for semi-structured problems. New tools are needed to explore, analyze, and visualize the solution space of a problem with respect to multiple analytical models and criteria. In this research we explore the utility of genetic algorithms as an effective means to: (1) search the solution space of geographical problems; (2) visualize the spatial ramifications of alternative criteria spaces; and (3) identify compromise solutions. |
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