@article{KoenigKnecht,
  author    = {K{\"o}nig, Reinhard and Knecht, Katja},
  title     = {Comparing two evolutionary algorithm based methods for layout generation: Dense packing versus subdivision},
  series = {Artificial Intelligence for Engineering Design, Analysis and Manufacturing},
  journal   = {Artificial Intelligence for Engineering Design, Analysis and Manufacturing},
  pages     = {285 -- 299},
  abstract  = {We present and compare two evolutionary algorithm based methods for rectangular architectural layout generation: dense packing and subdivision algorithms.We analyze the characteristics of the two methods on the basis of three floor plan sce- narios. Our analyses include the speed with which solutions are generated, the reliability with which optimal solutions can be found, and the number of different solutions that can be found overall. In a following step, we discuss the methods with respect to their different user interaction capabilities. In addition, we show that each method has the capability to generate more complex L-shaped layouts. Finally,we conclude that neither of the methods is superior but that each of them is suitable for use in distinct application scenarios because of its different properties.},
  subject      = {Architektur},
  language  = {en}
}
@article{Koenig,
  author    = {K{\"o}nig, Reinhard},
  title     = {Urban Design Synthesis for Building Layouts : Urban Design Synthesis for Building Layouts based on Evolutionary Many-Criteria Optimization},
  series = {International Journal of Architectural Computing},
  journal   = {International Journal of Architectural Computing},
  doi       = {10.1260/1478-0771.13.3-4.257},
  pages     = {257 -- 270},
  abstract  = {When working on urban planning projects there are usually multiple aspects to consider. Often these aspects are contradictory and it is not possible to choose one over the other; instead, they each need to be fulfilled as well as possible. In this situation ideal solutions are not always found because they are either not sought or the problems are regarded as being too complex for human capabilities.To improve this situation we propose complementing traditional design approaches with a design synthesis process based on evolutionary many-criteria optimization methods that can fulfill formalizable design requirements. In addition we show how self-organizing maps can be used to visualize many-dimensional solution spaces in an easily analyzable and comprehensible form.The system is presented using an urban planning scenario for the placement of building volumes.},
  subject      = {Design synthesis},
  language  = {en}
}