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The experiment generated design journals with all the steps these designers undertook in solving a design problem which included thermal comfort, energy efficiency targets and the testing of passive design strategies.
We demonstrate the efficiency of our approach by redesigning an experimentally generated design from the literature -xb[1].
The generated design solutions in the form of drawings are internally represented as hierarchical hypergraphs gathering design knowledge on which reasoning about designs can be based.
We also present a detailed example of the comparison of the performance of an automatically generated design against a manually generated implementation of the same computation.
Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design.
Consequently, not only it extracted known design information but also it successfully generated design strategies not to extract from the database.
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The properties of the generated designs are further investigated.
The framework consists of a computer program, which generates designs based on shape grammars, and rapid prototyping technologies that produce physical models of generated designs.
The generated designs and the algorithm are evaluated and recommendations are given.
It concludes by showing ways to use the same AD principles to qualitatively evaluate generated designs.
The automatically generated designs enable experts to focus their efforts on the difficult parts of a network design problem.
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