algorithmic modeling for Rhino



goat is an optimization solver add-on component. It perfectly complements galapagos, David Rutten's evolutionary solver based on a randomized core. goat pursues a mathematical rigorous approach and relies on gradient-free optimization algorithms, delivering fast and deterministic results. At every run, goat will yield the same optimal result.

goat is a drop-in replacement for galapagos. It is based on David Rutten's galapagos GUI and interfaces NLopt, a collection of mathematical optimization libraries.


For getting started with optimization in parametric modelling environments in general and with goat in special, check out our presentation slides on Geometry and Optimization with several comprehensive examples.

Once you are familiar with the basics of optimization, head over to our comprehensive documentation on goat's different configuration options.

Members: 166
Latest Activity: Sep 20, 2023

goat - Known Issues

goat is a young project under steady development. As of version 3.0, the following issues are known:

  • none.

For Grasshopper 0.9.0014+ on Rhino 5, use goat version 3.0

For Grasshopper 0.9.x on Rhino 4, go with goat version 1.4.

For Grasshopper 0.8.x, use goat version 1.2.

Discussion Forum

GOAT algorithms from C# box 2 Replies

Hi,Thank you for sharing this great plugin !Is there a way to call the optimization algorithms of GOAT inside a C# script ?Many thanksContinue

Tags: goat

Started by Xavier Tellier. Last reply by Xavier Tellier May 27, 2019.

shell line optimization by goat

Hi,I am a very new user of karamba so the goat. currently I'm working on shell stress line analysis. I'm reproducing the example of "Input surfaces and analyze as a shell" mentioned in the karamba…Continue

Started by Hass A. Apr 2, 2019.

goat 3.0 - an optimization solver component

Hi everybody,We are happy to announce version 3.0 of our fast and versatile optimization component goat. Download is available as usual…Continue

Tags: optimization, release, lists, pools, gene

Started by Simon Flöry Nov 22, 2016.

Inequality Constraints??? 1 Reply

Hi,Firstly, thanks for the great tool! I really appreciate the greatest descent approach which is great for situations where computation times are long and genetic algorithms are not feasible!My…Continue

Started by Sam Gregson. Last reply by Simon Flöry Oct 2, 2015.


Members (166)






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