Metaheurística algoritmo genético na solução de modelos de planejamento florestal RES UMOObjetivou-se testar a metaheurística Algoritmo Genético (AG) e avaliar sua eficácia e eficiência na solução de problemas de planejamento florestal, comparado a resultados obtidos pelo software CPLEX. Para analisar o efeito dos diferentes parâmetros no desempenho do AG, foi empregado o delineamento inteiramente casualizado no arranjo fatorial, em que os fatores considerados foram: três tamanhos de população inicial (Pini), três taxas de crossing-over (Tcross) e dois métodos de crossing-over (Mcross). Nos casos em que as interações foram significativas pelo teste F em nível de 5% de probabilidade, foram realizados os desdobramentos dos fatores, testando-se as diferenças entre as médias pelo teste de Tukey, em nível de 5% de probabilidade. Como medida de eficácia e eficiência utilizou-se a distância percentual (distância entre a resposta do AG e a resposta exata) e o tempo de processamento, respectivamente. A população inicial é o fator que mais influencia o desempenho do AG em termos de distância e de tempo de processamento, de modo que para Pini maiores são encontrados maior proximidade da resposta do AG com a resposta exata e também maiores tempos de processamento. Palavras-chave: manejo florestal, otimização, heurísticasGenetic algorithm metaheuristic in the solution of forest management models AB S TR ACTThis work aimed to test the Genetic Algorithm (GA) metaheuristic evaluating its effectiveness and efficiency in the solution of this kind of problem, and comparing its results with those obtained by the software CPLEX. A completely randomized design, as the factorial arrangement, was used to analyze the effect of different parameters on GA performance. Sizes of initial population (Pini), crossing-over rates (Tcross) and crossing-over methods breaks (Mcross) were the analyzed factors. In the cases that the interactions were significant by F test (P < 0.05) the differences among the means were tested by Tukey test at 5% probability level. The percentage distance (distance between the answer of AG and the exact answer) and the time of processing were used as a measure of effectiveness and efficiency, respectively. The initial population is the factor that most influenced the AG performance considering percentage distance and processing time, so that for larger Pini are found greater proximity of GA response to the exact response and also greater processing times.
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