Designers and design researchers both agree that developing many feasible alternatives at the conceptual design stage is useful. In this paper we introduce generative configuration design (GCD) for conceptual design. We provide a partition of knowledge accessed during GCD and use the partitioned knowledge foundation to compare design tool architectures so that computational improvements can be made. We present an improved architecture for a GCD algorithm and implement it as a tool for office chair design. Subsequent examples show tradeoffs between computational load and design variety when applying constraints for behavior testing.

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