Food4Rhino

iGene is developed by the SD-II Lab (Sustainable Design and Intelligent Inference) at Tongji University for enhancing the efficiency of intelligent optimization in performance-based design.

The plugin optimizes the transfer of data, effectively improving the optimization efficiency of genetic algorithms when using surrogate models. It solves the issue of long model loading times every prediction within the Grasshopper environment.

Users can run any machine-learning surrogate model related to modeling optimization using this plugin and truly benefit from the acceleration provided by surrogate models.

We provide two examples: one for optimization under general conditions and the other for optimization using a surrogate model. It's worth noting that besides using surrogate models, iGene also performs exceptionally well in general optimization scenarios.

If you encounter any issues, you can provide feedback to the SD-II lab at tongjisdiilab@gmail.com or to the developer within the team at zhong.plusplus@gmail.com.