Millions of years ago a regular South American land iguana floated the 600 miles of ocean waters to the Galapagos Islands aboard debris or driftwood. From that species emerged two distinct types, the Galapagos Marine Iguana and the Galapagos Land Iguana. Darwin called the marine version “hideous looking…clumsy lizards” and the little godzillas manage to convey a gentle malevolence that belies their carefully tuned and incredible evolutionary adaptations. They became marine reptiles, exclusively feasting on underwater algae and seawater during the morning, warming themselves on the lava rocks the rest of the day. Their long claws give them purchase on the craggy underwater rocks during fierce underwater tides, while their spiky dorsal extrusions – coupled with their flattened, narrow tails – allow them to gracefully glide underwater. While their dark complexion is designed to better absorb the sun’s rays after a dive in the frigid waters. Even their stubby face is tuned, allowing them to quickly bite off algae with a series of sharp teeth on either side of their face. Prompt meals are a must, after 10 minutes of diving in cold Galapagos waters, their muscles will lock-up. But the best part is the unique nasal glands that allow them to expel the salt that gets ingested into their blood during underwater meals and produces what sounds like a sneeze followed by a torrent of salt water ejected from their nasal cavity. The salt blowing out of their noses gives them a distinct white coloration on the top of their heads. Imagine a field of the laconic, sluggish monsters slowly warming up on the rocky shore, silent except for the regular punctuations of streams of saltwater sneezes blasting themselves and their jumbled up neighbors. No question, they were my favorite.
The yellowish-brown Galapagos Land Iguana is largely vegetarian, living in arid regions of the islands and eating the prickly pear cactus. Due to the introduction of feral dogs and rats, they were rendered extinct on some islands during the last 60 years, however reintroduction efforts have been largely successful. Despite being two distinct species from different genera, marine and land iguanas can interbreed when sharing territory, however, the hybrid offspring is typically sterile.
Endemic Flightless Cormorants and the Blue-footed Booby first studied by Darwin in the Galapagos.
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…