Multivariate testing has recently emerged as a promising technique in web interface design. In contrast to the standard A/B testing, multivariate approach aims at evaluating a large number of values in a few key variables systematically. The Taguchi method is a practical implementation of this idea, focusing on orthogonal combinations of values. It is the current state of the art in applications such as Adobe Target. This paper evaluates an alternative method: population-based search, i.e. evolutionary optimization. Its performance is compared to that of the Taguchi method in several simulated conditions, including an orthogonal one designed to favor the Taguchi method, and two realistic conditions with dependences between variables. Evolutionary optimization is found to perform significantly better especially in the realistic conditions, suggesting that it forms a good approach for web interface design and other related applications in the future.

PDF

In Proceedings of the 2020 IEEE Congress on Evolutionary Computation, 2020.

@inproceedings{jiang:cec20, title={A Comparison of the Taguchi Method and Evolutionary Optimization in Multivariate Testing}, author={Jingbo Jiang and Diego Legrand and Robert Severn and Risto Miikkulainen}, booktitle={Proceedings of the 2020 IEEE Congress on Evolutionary Computation}, month={ }, url="http://www.cs.utexas.edu/users/ai-lab?jiang:cec20", year={2020} }

Video