MACHINE LEARNING MODELS FOR DIRECTED CURATION OF DESIGN SOLUTION SPACE
1 online resource (54 pages) : PDF
University of North Carolina at Charlotte
The expanding role of computational models in the process of design is producing exponentially growing parameter spaces. As designers, we must create and implement new methods for searching these parameter spaces considering not only quantitative optimization metrics but also qualitative features. This paper proposes a methodology leveraging pattern modeling properties of artificial neural networks to capture designer’s inexplicit selection criteria and create user-selection based fitness functions for a genetic solver. Through emulation of learned selection patterns, fitness functions based on trained networks provide a method for qualitative evaluation of designs in the context of a given population. The application of genetic solvers for the generation of new populations based on the trained networks selections creates emergent high-density clusters in the parameter space, allowing for the identification of solutions which satisfy the designer’s inexplicit criteria.
Dickey, RachelGero, JohnEllinger, JeffersonHadzikadic, Mirsad
Thesis (M.S.)--University of North Carolina at Charlotte, 2017.
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