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.
Website: https://www.rechenraum.com/en/goat.html
Members: 166
Latest Activity: Sep 20, 2023
goat is a young project under steady development. As of version 3.0, the following issues are known:
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.
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.
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.
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.
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.
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