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Journal of Engineering and Applied Sciences
Year: 2017 | Volume: 12 | Issue: 6 SI | Page No.: 7779-7784
DOI: 10.36478/jeasci.2017.7779.7784  
Evolutionary Computation: Characterization of the Genetic Algorithm in the Optimization of the Binary Knapsack Problem
Pedro Ramiro Brito Portero
 
Abstract: In the area of artificial intelligence, Genetic Algorithms (GAs) are based on the genetic process of biological evolution are adaptive logic processes that solve different problems of search and combinatorial optimization in engineering, the objective of this research is to characterize the behavior of the incomplete Genetic algorithm, in a classical NP-hard combinatorial optimization problem such as the binary Knapsack problem, establishing parameters or arguments of measurement in different probability ranges, allows us to establish analytical strategies that guarantee a range of minimum error, the proposed application allows maximizing the profit of the Knapsack and optimize the quantity of products that must be stored in it, presenting the results analytically and graphically.
 
How to cite this article:
Pedro Ramiro Brito Portero , 2017. Evolutionary Computation: Characterization of the Genetic Algorithm in the Optimization of the Binary Knapsack Problem. Journal of Engineering and Applied Sciences, 12: 7779-7784.
DOI: 10.36478/jeasci.2017.7779.7784
URL: http://medwelljournals.com/abstract/?doi=jeasci.2017.7779.7784