You are currently viewing an archived version of Topic Genetic Algorithms and Evolutionary Computing.
If updates or revisions have been published you can find them at Genetic Algorithms and Evolutionary Computing.
Author and Citation Info:
DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., and Wentz, E. (2006). Genetic algorithms and artificial genomes. The Geographic Information Science & Technology Body of Knowledge. Washington, DC: Association of American Geographers. (2nd Quarter 2016, first digital).
Learning Objectives:
Create an artificial genome that can be used in a genetic algorithm to solve a specific problem
Describe a cluster in a way that could be represented in a genome
Explain how and why the representation of a GA’s chromosome strings can enhance or hinder the effectiveness of the GA
Use one of the many freely available GA packages to apply a GA to implement a simple genetic algorithm to a simple problem, such as optimizing the location of one or more facilities or optimizing the selection of habitat for a nature preserve geospatial pattern optimization (such as for finding clusters of disease points)
Describe a potential solution for a problem in a way that could be represented in a chromosome and evaluated according to some measure of fitness (such as the total distance everyone travels to the facility or the diversity of plants and animals that would be protected) genome
You are currently viewing an archived version of Topic Genetic Algorithms and Evolutionary Computing. If updates or revisions have been published you can find them at Genetic Algorithms and Evolutionary Computing.
DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., and Wentz, E. (2006). Genetic algorithms and artificial genomes. The Geographic Information Science & Technology Body of Knowledge. Washington, DC: Association of American Geographers. (2nd Quarter 2016, first digital).