I’ve used genetic algorithms for form finding with a previous project, and that time I was using a tenuous connection between catia, modeFrontier and Robot. So I was excited to see grasshopper begin to natively implement an evolutionary solver with Galapagos. As an initial experiment I started with a classic, something simple – I wanted to find a tessellated form that would enclose the maximum volume using the smallest surface area. I’d like to think that this would produce something unexpected, but it’s pretty much the definition of a sphere. I set up the parametric model to wiggle all over the place with various triangulated densities and differing number sided polygons at each joining segment. My hypothesis was that the form would tend toward symmetry and evolve into the aforementioned spherical shape. I believed that the polygons would tend toward the most sides possible to more closely approximate a circle, later generations evolving away from a triangle toward an icosagon. (Just like on Flatland!)

A couple of observations: Galapagos pretty quickly found the overall shape – smaller radii at the extremes and bulging in the middle – the beginning of a sphere. However, while it tended toward bilateral symmetry, it kept a kink in the first segment that prevented the shape from being perfectly symmetrical. I think the solver got stuck in a local minimum as opposed to a global minimum. Perhaps with a higher mutation level or letting it run for a longer amount it could have jumped out of this. On further checks I found that it was correct, after 30 generations and over 2500 iterations, the surviving croissant-like shape of the optimal designs did have a better SF:V ratio than a perfectly symmetrical design. Perhaps it had something to do with the setup of the parametric model or the way the facets resolve themselves at the extremities?

But in general my hypothesis was proven correct. Which leads to the initial problem with Galapagos. There are a lot of opportunities with this type of experiment and people more clever than me will surely do them, but when you can only solve for one objective it becomes difficult to create truly complex solutions. For instance, with my surface area/volume problem there is only one true pareto solution. Eventually Galapagos will find it, or with enough time and a calculator I could calculate this myself. There is one single, optimal solution, it’s just hidden somewhere amongst a number of parametric sliders. Unless you start getting into multiple, competing objectives, then the pareto point becomes a curve and there are multiple valid solutions, each one involving certain trade offs and a criteria for selection. Say you wanted to find a form with the minimum srf area:volume ratio, but also that form had to have the fewest structural members, or provide the most shade on June 21st, or spatially provide the most potential revenue stream for a project stakeholder. That’s when it gets really interesting and opens the possibility for a design space that includes high performing, unexpected results. It’s a great start, and I can’t wait to see Galapagos evolve.

Download the grasshopper definition for version 0.8.0004 here: http://gracefulspoon.com/downloads/Grasshopper_GALAPAGOS_TEST.rar

Quick Project Desciption: Airports typically attempt to be all things to all people, resulting in general inefficiency and awkward relationships between program spaces. By seeking new opportunities via trade-offs, for instance a tourist class passenger waiting longer but flying for free, or a business class passenger’s ticket price rises while he waits less in a more luxurious setting, a new circulation map and airport space is created that addresses these disparate groups needs. Optimal relationships between airlines, airport, and users are handled through parametric models and genetic algorithms.

What is the metric for a good design? Or rather, now that parametric modelling allows us to easily create thousands of variations of a given design, how do we chose the “correct” one?

First, Creating a parametric model in catia, whose inputs are optimized through the engineering program modeFrontier with additional structural finite element analysis coming from autodesk’s newly aquired robot. The challenge became how to convert your design position, parti, whatever, into a quantifiable metric that the software can optimize for. For instance, to optimize for material efficiency, you could let the software optimize a shape for maximize volume with minimal surface area. After 3000 designs you’d have a sphere, but things can get very complex fast when you begin optimizing for competing objectives. See our complete studio blog here. Project description…
I was drawn to the metrics of passenger economy and profit. Airports typically attempt to be all things to all people, resulting in general inefficiency and awkward relationships between program spaces and passengers, especially business and tourist class. By seeking new opportunities via tradeoffs, for instance a tourist class passenger waiting longer but flying for free, or a business class passenger’s ticket price rises while creating multiple, separate dedicated entry points that allow shorter waits, a new optimized circulation map presents itself.

Each hanging element is a program + structural column connected by a circulation tube. Within the circulation tube tourist class passengers have the opportunity to fly for free, passing through each commercial program space. One objective is to maximize the length of the tube – thereby allowing more passengers to fly for free maximizing the airports ancillary profits. Another objective is to create an unobstructed space for business class passengers requiring few of the program spaces to touch the ground but rather hang, allowing business class passengers to freely pass through below. The more columns that touch the ground, the more structurally stabe the ceiling space frame becomes, allowing more housing towers above. The program mediates between these competing objectives finding high-performing, unexpected solutions and it becomes the role of the user to rank and chose designs based on desired criteria. Most housing = most columns = fewer business class travellers, etc…

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