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

The size and complexity of websites have grown significantly during the recent years. In line with this growth, the need to maintain most of the resources has been intensified. Content Management System (CMS) is software that has been presented in accordance with the increased demands of the users. With the advent of CMSs, factors such as domains, pre-designed module development, graphics, optimization, and alternative support have become the factors that influenced the cost of the 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 the websites designed by CMSs. The proposed method uses a combination of Genetic Algorithm (GA) and Multi-layer Perceptron (MLP). The results obtained are 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 these results, the Kappa coefficient on testing dataset equals 0.82% for the proposed method, 0.06% for GA, and 0.54% for MLP Artificial Neural Network (ANN). Based upon 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 CMSs.