Galapagos 101 - Fundamentals
Simulation and Analysis
In this 101 series about evolutionary computing or evolutionary solving, the basics of evolutionary solving is explained and how and why it can be useful for you in everyday practice.
Arie-Willem de Jongh
In this 101 series about evolutionary computing or evolutionary solving, I’m going to go over the basics of evolutionary solving and how and why it can be useful for you in everyday practice.
This plugin comes standard within Grasshopper and is very helpful to optimize complex design questions with lots of variables. A couple of examples would be; optimizing a building mass to maximize its views, optimizing a facade system according to solar radiation or use it to optimize member sizes in a structural space frame to reduce weight and cost.
We’re going to start with a basic example where we model a random landscape with peaks and valleys and let Galapagos find the highest peak. This example is perfect for you to familiarize with the concept and the workings of an evolutionary solver.
After this, we’ll take a look at a simple real-life scenario where we’ll optimize a set of plot outlines by equalizing their floor areas. We’ll end with adding, normalizing and weighting multiple fitness values into Galapagos. In this case, floor areas and the shape of the plot outlines.
1.- Introduction and brief history of evolutionary solving02min 00seg
2.- Setting up our landscape12min 17seg
3.- Running the Galapagos solver13min 14seg
4.- Optimizing square meters of a building lot11min 59seg
5.- Using the Galapagos solver to equalize the individual lots14min 01seg
6.- Optimizing multiple fitness values17min 35seg