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  Volume 2, Number 3, August 2017 , pages 241-256

Genetic algorithms for balancing multiple variables in design practice
Bomin Kim and Youngjin Lee

    This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant
Key Words
    genetic algorithm; multi-objective optimization; parametric and evolutionary design; BIM
Bomin Kim: Architecture, Sasaki Associates, 64 Pleasant Street, Watertown, Massachusetts, 02472 United States
Youngjin Lee: Department of Architecture, Boston Architectural College, 320 Newbury Street, Boston, Massachusetts, 02115, United States

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