Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/14682
metadata.revistascielo.dc.title: USING GENETIC ALGORITHM IN FOREST PLANNING CONSIDERING ITS SELECTION OPERATORS
metadata.revistascielo.dc.creator: Gomide, Lucas Rezende
Arce, Julio Eduardo
Silva, Arinei Carlos Lindbeck da
metadata.revistascielo.dc.subject: Metaheuristic, combinatory analysis, model type I
metadata.revistascielo.dc.publisher: CERNE
CERNE
metadata.revistascielo.dc.date: 19-May-2015
metadata.revistascielo.dc.identifier: http://www.cerne.ufla.br/site/index.php/CERNE/article/view/180
metadata.revistascielo.dc.description: This study tested and analyzed four selection operators (Elitist, Tournament, Roulette wheels and Bi-classist) and defined the best one.  The forest planning problem test was based on the Johnson & Schermann (1977) type I model encompassing 52 eucalyptus stands, where 254 forest management prescriptions were created. The genetic algorithm (GA) was built in Visual Basic® Microsoft® and its sets of parameters were: initial population (300), crossover (10%), mutation (10%) and replacement (60%). The measuring variables were: minimum, median and maximum values; coefficient of variation for the fitness and the processing time.  It was also applied the nonparametric Kruskal-Wallis test with 5% of the probability to check the differences among selection operators of 30 samples. The results showed that the selection operators presented different efficiency and effectiveness according to Kruskal-Wallis test for 5% of probability. The decreasing sequence of efficiency was: Roulette wheels, Tournament, Elitist and Bi-classist. The lower percentage deviations matched from the exact solution were: 2.75% (Elitist), 2.15% (Tournament), 0.90% (Roulette wheels) and 2.40% (Bi-classist). The best selection operator tested was the one that follows the Roulette wheels. 
metadata.revistascielo.dc.language: por
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