Researchers propose three main algorithms related to these operations: combinatorial equilibrium modeling is used in the generation stage (similar to Geometric Equilibrium through 3D Graphical Statics approach), self-organising maps to clusters the outputs, and gradient-boosted trees to learn user preferences and incorporate subjective criteria during the election stage.
generation, clustering, evaluation, election, and regeneration
combine human cognition with machine-driven computation, joining humans’ subjective evaluation and selection capacity with machines’ ability to handle large datasets
structural design framework that allows generating multiple novels, diversified, and structurally informed design options that could be handled masterfully by joining algorithms of Machine Learning and human decision-making to select one design option fulfilling all the designer’s qualitative and quantitative criteria
having a non-typological approach towards design is an important requirement
design exercises that rely solely on parametrization will encounter a significant challenge to adopt an approach that goes beyond typologies