Keywords
decision support systems, forest management, genetic algorithms, ned-2, silviculture treatment scheduling, forest vegetation simulator (fvs)
Start Date
1-7-2006 12:00 AM
Abstract
This paper describes research on the use of a multiobjective genetic algorithm (GA) to optimize prescriptive treatment plans for forest management. The algorithm is novel, in that (1) the plans generated by the algorithm are highly specific, stating precisely when and where treatments are to be applied; and (2) logical rules and inference engines developed for a decision support system are used to evaluate the fitness of each plan. Fitness is based upon satisfaction of varied and often incompatible goals. The current (generational) GA has been compared in experiments to hill-climbing and simulated annealing algorithms, as well as to a steady-state GA. In a separate experiment, a plan generated by the GA is compared to one produced by a human expert. In these experiments, the GA has faired well.
Prescriptive Treatment Optimization Using a Genetic Algorithm: A Tool for Forest Management
This paper describes research on the use of a multiobjective genetic algorithm (GA) to optimize prescriptive treatment plans for forest management. The algorithm is novel, in that (1) the plans generated by the algorithm are highly specific, stating precisely when and where treatments are to be applied; and (2) logical rules and inference engines developed for a decision support system are used to evaluate the fitness of each plan. Fitness is based upon satisfaction of varied and often incompatible goals. The current (generational) GA has been compared in experiments to hill-climbing and simulated annealing algorithms, as well as to a steady-state GA. In a separate experiment, a plan generated by the GA is compared to one produced by a human expert. In these experiments, the GA has faired well.
