A New Hybrid model of Multi-layer Perceptron Artificial Neural Network and Genetic Algorithms in Web Design Management Based on CMS

Abstract

The size and complexity of websites have grown significantly during recent years. In line with this growth, the need to maintain most of the resources has been intensified. Content Management Systems (CMSs) are software that was presented in accordance with increased demands of users. With the advent of Content Management Systems, factors such as: domains, predesigned module's development, graphics, optimization and alternative support have become factors that influenced the cost of software and web-based projects. Consecutively, these factors have challenged the previously introduced cost estimation models. This paper provides a hybrid method in order to estimate the cost of websites designed by content management systems. The proposed method uses a combination of genetic algorithm and Multilayer Perceptron (MLP). Results have been evaluated by comparing the number of correctly classified and incorrectly classified data and Kappa coefficient, which represents the correlation coefficient between the sets. According to the obtained results, the Kappa coefficient on testing data set equals to: 0.82 percent for the proposed method, 0.06 percent for genetic algorithm and 0.54 percent for MLP Artificial Neural Network (ANN). Based on these results; it can be said that, the proposed method can be used as a considered method in order to estimate the cost of websites designed by content management systems.