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Discriminant Analysis As A Function Of Predictive Learning To Select Evolutionary Algorithms In Intelligent Transportation System
In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classificationdoi:10.5281/zenodo.1123578 fatcat:z7p34welpbfqpapmmyoelavdwa