A Computational Design System with Cognitive Features Based on Multi-objective Evolutionary Search with Fuzzy Information Processing

  • Michael S. Bittermann


A system for architectural design is presented, which is based on combining a multi-objective evolutionary algorithm with a fuzzy information processing system. The aim of the system is to identify optimal solutions for multiple criteria that involve linguistic concepts, and to systematically identify a most suitable solution among the alternatives. The system possesses cognitive features, where cognition is defined as final decision-making based not exclusively on optimization outcomes, but also on some higher-order aspects, which do not play role in the pure optimization process. That is, the machine is able to distinguish among the equivalently valid solution alternatives it generated, where the distinction is based on second order preferences that were not pin-pointed by the designer prior to the computational design process. This is accomplished through integrating fuzzy information processing into the multi-objective evolutionary search, so that second-order information can be inductively obtained from the search process. The machine cognition is exemplified by means of a design example, where a number of objects are optimally placed according to a number of architectural criteria.


Pareto Front Multiobjective Optimization Pareto Optimal Solution Cognitive Feature Priority Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Netherlands 2011

Authors and Affiliations

  • Michael S. Bittermann
    • 1
  1. 1.Delft University of TechnologyThe Netherlands

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