@inproceedings{HaggAsterothB{\"a}ck2019,
  author    = {Hagg, Alexander and Asteroth, Alexander and B{\"a}ck, Thomas},
  title     = {Modeling User Selection in Quality Diversity},
  booktitle = {GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019},
  isbn      = {978-1-4503-6111-8},
  doi       = {10.1145/3321707.3321823},
  institution = {Fachbereich Informatik},
  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}
}