Genetic programming (GP), as a predictive data analytic tool, has difficulties dealing with high-dimensional problems. Therefore, some GP variants have been proposed for this type of problem, such as multi-stage GP (MSGP). Filter-based feature selection is commonly used in the literature for various machine learning purposes. However, its application for GP is overlooked due to GP's capability to operate as a wrapper-based feature selection while trying to find an optimal expression of the target variable via a functional combination of predictors. The effectiveness of wrapper- and filer-based feature selection approaches in machine learning has been the subject of a long-standing debate in the literature. This study aims to introduce an efficient feature selection approach and couple it with MSGP in order to handle high-dimensional problems. In addition, the stages of the GP are systematically ordered based on the variables' information. The proposed approach is tested against five real high-dimensional datasets. The results show that GP's inherent wrapper feature selection ability can be advanced further by using a filter-based feature selection approach to shrink the search space, which results in improving computational costs, expression complexity and the accuracy of MSGP.

The work consists of two subapproaches. In the first approach, an analytical model is developed using trapezium fuzzy numbers in decision-making problems for an Internet of Things-based water distribution network. The second phase explains the integration of the previous phase with the MCDM-based location routing protocol (M-LRP). The water distribution network has three components static water source, the utility center (UC) which can be located in the proper position, and the consumer. The objective of this work is to select an optimal route between the UC and the consumer by considering multiple criteria. The simulation result shows that the proposed multicriterion-based decision-making (MCDM)-based routing protocol outperforms both existing MCDM-based and non-MCDM-based routing schemes. The proposed model outperforms the existing models like non-MCDM-based and MCDM-based routing protocols by 51% and 11%, respectively.