@inproceedings{HaggAsterothBaeck2019, author = {Alexander Hagg and Alexander Asteroth and Thomas B{\"a}ck}, title = {Modeling User Selection in Quality Diversity}, series = {GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019}, publisher = {ACM}, address = {New York, NY, USA}, isbn = {978-1-4503-6111-8}, doi = {10.1145/3321707.3321823}, pages = {116 -- 124}, year = {2019}, abstract = {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.}, language = {en} }