Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

Optimal machining parameters can lead to considerable savings in manufacturing problems. In this paper, to deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost (UC) in parallel turnings, we propose a novel optimization approach which divides this complicated problem into several sub-problems. Then an estimation of distribution algorithm (EDA) is developed to search the optimal results for each sub-problem. Computer simulations show that the proposed approach is efficient in searching the optimal solutions to reduce significantly the unit production cost.