*Result*: DCGAEL: An Optimized Ensemble Learning using a Discrete-Continuous Bi-Level Genetic Algorithm.

*Ensemble learning encompasses methods that generate many well-diversified predictors and aggregates their results to perform a better prediction. These predictors are usually weak and low-cost for obtaining when they are alone. However, they reveal excellent performance when they are skillfully used together in the form of a learning architecture. Metaheuristic methods have been used to form such architecture optimally during recent years. Along this stream, in this paper, a bi-level optimization based on discrete-continuous genetic algorithm is utilized to enhance the performance of an ensemble learning metaalgorithm which benefits decision tree classification. Feature selection and tree model constructing for any ensemble member are done by the metaheuristic method. It allows us to have advantages of tree-based prediction models, ensemble learning, and solution optimality simultaneously. The proposed system is compared to some well-known ensemble learning methods. Results show significant superiority of the proposed system in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]*