  The initial phase in real world engineering optimization and design is a
process of discovery in which not all requirements can be made in advance, or
are hard to formalize. Quality diversity algorithms, which produce a variety of
high performing solutions, provide a unique chance to support engineers and
designers in the search for what is possible and high performing. In this work
we begin to answer the question how a user can interact with quality diversity
and turn it into an interactive innovation aid. By modeling a user's selection
it can be determined whether the optimization is drifting away from the user's
preferences. The optimization is then constrained by adding a penalty to the
objective function. We present an interactive quality diversity algorithm that
can take into account the user's selection. The approach is evaluated in a new
multimodal optimization benchmark that allows various optimization tasks to be
performed. The user selection drift of the approach is compared to a state of
the art alternative on both a planning and a neuroevolution control task,
thereby showing its limits and possibilities.
