by Laurens Verspeek
VU University Amsterdam
Nowadays, there are over 1 billion websites online in the world wide web. Modern web applications need to adapt to many different users. The user preferences are often hard to completely determine at design time. Therefore, these web applications are typically tested and refined at run-time to better determine the user preferences or to discover changed user preferences. These user-intensive websites increasingly rely on online controlled experiments, where different variants are being tested on live visitors. Technological advances in web design imply a large search space. A common strategy is sampling this huge search space. Current optimization strategies, such as A/B testing or multivariate testing, use manual sampling, and are therefore time-consuming for the designer. The variants the designer samples from the large search space are static and cannot change during the test. After the test results are interpreted, the designer can setup new variants for the next static test. In this thesis we propose an online automated multivariate web design optimization system (AMOS) based on a genetic algorithm. This optimization technique will take away the manual part in website optimization by automating the sampling from the search space with a genetic algorithm. We will use a modified genetic algorithm, which uses fluid generations and keeps track of previous generations. By doing this we will get dynamic variants during the test and faster results. To validate the new optimization algorithm, it is implemented and tested on a live website with real users.
System architecture of the implementation of AMOS into a framework
