Grasshopper Advanced Practice #02 | Galapagos


  1. Galapagos
    1. What is “Galapagos” ?
    2. Pipeline | example
    3. Weighting | example
  2. Applications
    1. Maximize height of catenary shape | example
    2. Maximize surface area of iso surface | example


What is “Galapagos” ?

Galapagos is one of the functions in Grasshopper to use Genetic Algorithm to optimize parameters. Basically, the parameter optimization is to be solved in physical or mathematic way, but some problems cannot be solved in that kind of linear methods. Genetic Algorithm is not so clever/fast method to solve problems but is powerful to such difficult problems to solve.

For more detail, refer official documentation here.


スクリーンショット 2014-12-16 17.06.20

There are 2 sets of parameters to use galapagos:

  • genetic inputs
  • parameter to fit (minimize / maximize)

and galapagos searches the best genetic inputs to produce minimized/maximized parameter as result.


To evaluate certain situation, sometimes we need to deal several parameters. But in galapagos, only one parameter is available for the evaluation parameter to fit. Because of it, it is important how to combine several parameters into one parameter mathematically. Basically, we can just multiplying and subdividing each parameter, and also, we can involve or exponentiate to empower it.


  • Maximize height of catenary shape
  • Maximize surface area of iso surface