Genetic Algorithm For Parameter Definition in Forest Harvest Optimization
Carlos Eurico Galvão Rosa, Gustavo Valentim Loch, Cassius Tadeu Scarpin · Revista de Gestão Social e Ambiental · 2025
Objective: The study aims to explore the use of genetic algorithms for parameter calibration in mathematical modeling for forest harvest optimization, addressing the need for adjustments due to the initial unavailability of essential parameters. Theoretical Framework: The research is based on similar cases requiring parameter calibration using several techniques, genetic algorithms among that technique, as the basis for adjusting mathematical models in forest settings. Method: Utilizing real data on productivity and plot positioning, a mathematical model was constructed, and genetic algorithms were applied to determine the missing parameters. Results and Discussion: The results indicate that genetic algorithms effectively defined and adjusted model parameters, resulting in more efficient solutions for forest harvesting. Research Implications: The study significantly contributes to optimization practices in forest harvests, enabling a more accurate and adaptable model for different scenarios. Originality/Value: The originality lies in the application of genetic algorithms in this context, providing an innovative tool for forest operations optimization, promoting more sustainable and efficient management.