Self-modernization of website design elements using a genetic algorithm
Ключові слова:genetic algorithm, site, mixing, design, conversion, object
Problem. Nowadays, computing devices and various information technologies are widely used to solve tasks of a wide variety from theoretical to practical. Now it becomes important to solve tasks for which it is impossible to find the exact solution, tasks for which there is no general solution in principle, as well as еру tasks for which it is sufficient to choose a solution with some degree of application. Goal. To explore the application of the genetic algorithm to websites to allow them to modernize some of their design elements on their own. To determine which designs are better, it is necessary to maximize conversion in the goal function of the genetic algorithm. Methodology. A sample of a website that is implemented from the following blocks: headline, text description, image, buy button, and ad, is considered. Each of these blocks will have a genome that characterizes the following properties of this object: positioning, height and width, color, shadow, font size, hover effects, and more. Results. The article presented the study of the application of the genetic algorithm to websites, and the perspectives for independent improvement of some elements and its design by sites were considered. Originality. A genetic algorithm has been developed that will allow you to mix selected objects of web pages. Practical value. Practical tools for determining the phenotype of an object were analyzed, and this will allow the site design to evolve to a more conversion level.
Voronovskiy G. K. Geneticheskiye algoritmy, iskusstvennyye neyronnyye seti i problemy virtual’noy real’nosti [Genetic algorithms, artificial neural networks and virtual reality problems], Kharkov, 1997. 112 p. [in Russian]
MartíNez-Torres M. R., Toral S. L., Palacios B., Barrero F. An evolutionary factor analysis computation for mining website structures. Expert Systems with Applications: An International Journal, 2012. №14, pp. 11623-11633.
Moataz A. Ahmed, Fakhreldin Ali, Multiple-path testing for cross site scripting using genetic algorithms. Journal of Systems Architecture: the EUROMICRO Journal, 2016. №.64. pp. 50-62.
Vijay Kumar, Kumar Dilip, Multi-criteria website optimisation using NSGA-II. International Journal of Business Information Systems, 2016. №21, pp. 418-438.
Yan-Kwang Chen, Fei-Rung Chiu, Hung-Chang Liao, Chien-Hua Yeh. Joint optimization of inventory control and product placement on e-commerce websites using genetic algorithms. Electronic Commerce Research, 2016. №16, pp. 479-502.
Ivory M. Y., Megraw, R. Evolution of web site design patterns. ACM Transactions on Information Systems, 2005. №23, pp. 463-497.
Lavie T., Tractinsky N. Assessing Dimensions of Perceived Visual Aesthetics of Web Sites. International Journal of Human-Computer Studies, 2004. №60, pp. 269-298.