  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. 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 in the future.
