Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems
Genetic algorithms (GAs) have demonstrated success in solving spatial forest planning problems. We present an adaptive GA that incorporates population-level statistics to dynamically update penalty functions, a process analogous to strategic oscillation from the tabu search literature. We also explo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2009-01-01
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| Series: | International Journal of Forestry Research |
| Online Access: | http://dx.doi.org/10.1155/2009/527392 |
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| _version_ | 1832550223306555392 |
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| author | Matthew P. Thompson Jeff D. Hamann John Sessions |
| author_facet | Matthew P. Thompson Jeff D. Hamann John Sessions |
| author_sort | Matthew P. Thompson |
| collection | DOAJ |
| description | Genetic algorithms (GAs) have demonstrated success in solving spatial forest planning problems. We present an adaptive GA that incorporates population-level statistics to dynamically update penalty functions, a process analogous to strategic oscillation from the tabu search literature. We also explore performance of various selection strategies. The GA identified feasible solutions within 96%, 98%, and 93% of a nonspatial relaxed upper bound calculated for landscapes of 100, 500, and 1000 units, respectively. The problem solved includes forest structure constraints limiting harvest opening sizes and requiring minimally sized patches of mature forest. Results suggest that the dynamic penalty strategy is superior to the more standard static penalty implementation. Results also suggest that tournament selection can be superior to the more standard implementation of proportional selection for smaller problems, but becomes susceptible to premature convergence as problem size increases. It is therefore important to balance selection pressure with appropriate disruption. We conclude that integrating intelligent search strategies into the context of genetic algorithms can yield improvements and should be investigated for future use in spatial planning with ecological goals. |
| format | Article |
| id | doaj-art-835080ba7a274181acccb969adfa9dd0 |
| institution | Kabale University |
| issn | 1687-9368 1687-9376 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Forestry Research |
| spelling | doaj-art-835080ba7a274181acccb969adfa9dd02025-02-03T06:07:19ZengWileyInternational Journal of Forestry Research1687-93681687-93762009-01-01200910.1155/2009/527392527392Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning ProblemsMatthew P. Thompson0Jeff D. Hamann1John Sessions2Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USAForest Informatics, Corvallis, OR 97339, USADepartment of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USAGenetic algorithms (GAs) have demonstrated success in solving spatial forest planning problems. We present an adaptive GA that incorporates population-level statistics to dynamically update penalty functions, a process analogous to strategic oscillation from the tabu search literature. We also explore performance of various selection strategies. The GA identified feasible solutions within 96%, 98%, and 93% of a nonspatial relaxed upper bound calculated for landscapes of 100, 500, and 1000 units, respectively. The problem solved includes forest structure constraints limiting harvest opening sizes and requiring minimally sized patches of mature forest. Results suggest that the dynamic penalty strategy is superior to the more standard static penalty implementation. Results also suggest that tournament selection can be superior to the more standard implementation of proportional selection for smaller problems, but becomes susceptible to premature convergence as problem size increases. It is therefore important to balance selection pressure with appropriate disruption. We conclude that integrating intelligent search strategies into the context of genetic algorithms can yield improvements and should be investigated for future use in spatial planning with ecological goals.http://dx.doi.org/10.1155/2009/527392 |
| spellingShingle | Matthew P. Thompson Jeff D. Hamann John Sessions Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems International Journal of Forestry Research |
| title | Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems |
| title_full | Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems |
| title_fullStr | Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems |
| title_full_unstemmed | Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems |
| title_short | Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems |
| title_sort | selection and penalty strategies for genetic algorithms designed to solve spatial forest planning problems |
| url | http://dx.doi.org/10.1155/2009/527392 |
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