Hybrid Intelligent Design of Morphological-Rank-Linear Perceptrons for Software Development Cost Estimation

This paper presents a hybrid intelligent method to design Morphological-Rank-Linear (MRL) perceptrons to solve the Software Development Cost Estimation (SDCE) problem. The proposed method uses a modified genetic algorithm (MGA) to determine the best particular features to improve the MRL perceptron performance, as well as its initial parameters. Furthermore, for each individual of MGA, a gradient steepest descent method is used to optimize the MRL perceptron parameters supplied by MGA. An experimental analysis is conducted with the proposed method using the Desharnais and Cocomo databases. In the experiments, two relevant performance metrics and a fitness function are used to assess the performance of the proposed method. The results obtained are compared to methods recently presented in literature.