"Although I more or less agree with that definition my personal view of Generative Design is slightly different. For lack of a better example I'm going to go with DNA. In its raw state, DNA contains all of the instructions by which life can be created. It will dictate the growth of cells as well as the type/characteristics of cells. Theoretically you could clone someone from their DNA and get an exact "copy" of that person (remember Dolly?). However, the end result of that clone would not be an exact copy. Why? The growth of cells is not dictated solely by DNA, but also by its interactions with its surroundings. Since the clone invariably develops in conditions that were different from the "source being" there will always be a difference between the two.
To me, this is the characteristic of generative design. Its not just a product of the parameters that generate it, but of the conditions that surround and interact with it. Even within that definition, there's a very thin line that separates parametric design from generative design, since both rely on defining their operations through external values. That thin line, to me, is parametric design being generated by a singular reference to an outside piece of data and generative design being generated by a multitude of references an some interpretation of those references in an interconnected manner.
I've moved towards defining GH as a Logical Modeler rather than parametric or generative. The main reason is that most parametric modelers rarely focus on the link between parameters as an artifact of their process. Because of this, their process, though parametrically based, is still embedded within model itself, which is certainly fine. GH does not operate in this manner. The creation of a GH definition is more of a representation of the logical steps used to create a given output, where as with parametric modelers, the representation of the inheritance of all parameters is not nearly as evident. Considering the control, amount of information that is immediately available by looking at a definition, and the interaction with those logical steps I would certainly argue that THAT is an extremely valuable aspect of the GH process. I could possibly argue that the GH definition itself is more valuable than the output it creates.
As to the Generative capabilities of parametric modelers and GH, it all depends on how you structure the interactions with external data. A 1 to 1 relationship between a value and an output is more parametric in my book, where the interaction between a multitude of values is more of a generative approach.
Just my 2 cents "
Best, Damien
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3 comments:
Hi Ryan,
This is interesting, but some assumptions of Damien are questionable specially in reference to nature.
". That thin line, to me, is parametric design being generated by a singular reference to an outside piece of data and generative design being generated by a multitude of references an some interpretation of those references in an interconnected manner. "
Nature evolves the dna through the test of life. So the internal generative system is also evolved through testing against external conditions.
Sivam Krish
Wow Ryan, didn't expect to see my whole post "transported" :) Glad you liked it.
Sivam,
I'm no expert on genetics, so I made some big assumptions/generalizations for the sake of an example. Basically I just wanted to get the point across that generative design constitutes a more complex references to outside sources than parametric design.
Self-Evolution within design is another property on top of that, and takes it to a much different level that would be more indicative of genetic behavior. However, I wouldn't consider that a prerequisite for generative design.
Best,
Damien
didnt know you were checking out the blog damien! I just liked the entire thread really on the gh forum... a discussion that should be going side-by-side with learning the software... judging by the speed these programs are evolving, we (as designers/academics) must evolve our language; that said, I think we will find the parametric-generative differentiation happening in the future in design and software.
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