Title :
Adaptive genetic algorithm-based forest harvest adjustment
Author :
Wang, MeiFang ; Li, JinMing
Author_Institution :
Coll. of Forestry Sci., Fujian Agri. & Fore. Univ., Fujian, China
Abstract :
Forest harvesting adjustment is a decision-making which is large and complex system. In this paper, we analysis the shortcomings of the traditional harvest adjustment problems, and establish the model of multi-target harvest adjustment. As intelligent optimization, adaptive genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective planning the settlement of the issue, specially in complex occasions where there are many objective functions and optimize variables, or non-linear mathematical expression is not clear, the conventional method is ineffective. In order to solve the problem of forest harvesting adjustment, this paper introduces a genetic algorithm to the Forest Farm of Qiujia Liancheng Longyan for adaptive forest harvesting adjustment firstly. And the experimental result not only shows that the method is feasible and effective, but also shows that it can provide satisfactory solution for policy makers.
Keywords :
decision making; forestry; genetic algorithms; Forest Farm of Qiujia Liancheng Longyan; adaptive genetic algorithm; decision making; forest harvest adjustment; intelligent optimization; multiobjective planning; Artificial intelligence; Computer applications; Convergence; Decision making; Educational institutions; Forestry; Genetic algorithms; Intelligent systems; Knowledge engineering; Optimization methods;
Conference_Titel :
Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-2196-1
Electronic_ISBN :
978-1-4244-2197-8
DOI :
10.1109/ISKE.2008.4730990