gkrr: a gravitational-based kernel ridge regression for software development effort estimation

Software development effort estimation (sdee) can be interpreted as a set of efforts to produce a new software system. to increase the estimation accuracy, the researchers tried to provide various machine learning regressors for sdee. kernel ridge regression (krr) has demonstrated good potentials to solve regression problems as a powerful machine learning technique. gravitational search algorithm (gsa) is a metaheuristic method that seeks to find the optimal solution in complex optimization problems among a population of solutions. in this article, a hybrid gsa algorithm is presented that combines binary-valued gsa (bgsa) and the real-valued gsa (rgsa) in order to optimize the krr parameters and select the appropriate subset of features to enhance the estimation accuracy of sdee. two benchmark datasets are considered in the software projects domain for assessing the performance of the proposed method and similar methods in the literature. the experimental results on desharnais and albrecht datasets have confirmed that the proposed method significantly increases the accuracy of the estimation comparing some recently published methods in the literature of sdee.