Ensemble learning is a multiple-classi&amp / #64257 / er machine learning approach which combines, produces collections and ensembles statistical classi&amp / #64257 / ers to build up more accurate classi&amp / #64257 / er than the individual classi&amp / #64257 / ers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this thesis, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed systems &amp / #64257 / nd an elegant way of boosting a bunch of classi&amp / #64257 / ers successively to form a better classi&amp / #64257 / er than each ensembled classi&amp / #64257 / er. AdaBoost algorithm employs a greedy search over hypothesis space to &amp / #64257 / nd a good suboptimal solution. On the other hand, this work proposes an evolutionary search with genetic algorithms instead of greedy search. Empirical results show that classi&amp / #64257 / cation with boosted evolutionary computing outperforms AdaBoost in equivalent experimental environments.