In this proof of concept, we’ll explore an example where we let the computer do the work for us. A parametric algorithm in grasshopper can work as a design tool where the user interacts with sliders and data. But in this example, we will explore a proof of concept where we let the computer present different outcomes.
The computer will iterate over many options to present a set of good results to the user. The decision framework is basically designed by the designer, because he/she can dictate to the computer which solutions are preferable.
This method can be used in cases such as:
- Creating a layout with as much daylight as possible for each object.
- Letting the computer create layout options for arranging paintings on a wall within certain constraints.
- Optimizing routing options for Corona constraints
The above examples require manipulating many values that affect each other. Sometimes the solutions are intuitively easy to find, but when the problem becomes more complex, it can take a long time. This is where an Evolutionary Solver comes in.
Grasshopper has the ability to let the computer manipulate all the variables in the setup and learn which values tend to give a better result. That, in a nutshell, is what we call an Evolutionary Solver. Each generation of results gives “experience” to the machine as it learns.
In this test setup we have 6 walls. We want to optimize them so that they are seen from 2 or more viewpoints as much as possible.
The walls can move along one axis and while moving they can block another wall from the viewpoints. In the composition of these walls, there are some optimal positions so that the views are blocked as little as possible from all viewpoints. We will find these by using the evolutionary solver within Grasshopper called Galapagos.
Naturally, there are still many manual design choices to be made. But as this example illustrates, it can be used early in the design phase to optimize the overall layout.
A YouTube video of this proof of concept can be found here: