Accelerating Interactive Evolutionary Algorithms through Comparative and Predictive User Models

Interactive Evolutionary Algorithms (IEAs) are a powerful explorative search technique that utilizes human input to make subjective decisions on potential problem solutions. But humans are much slower than computers and get bored and tired easily, limiting the usefulness of IEAs. Here two variations of a user-modeling approach are compared to determine if this approach can accelerate IEA search. The IEA system used for these comparisons is called The Approximate User (TAU). With TAU, as the user interacts with the IEA a model of the user’s preferences is constructed and continually refined. The two user-modeling approaches compared are: 1. learning a classifier which correctly determines which of two designs is better; and 2. learning a model which predicts a fitness score. Rather than having people do the user-testing, we propose the use of a simulated user as an easier means to test IEAs. The TAU IEA and both variants of its user models is compared against a basic IEA and it is shown that TAU is up to 2.7 times faster and 15 times more reliable at producing near optimal results.