algorithmic modeling for Rhino
Wallacei (which includes Wallacei Analytics and Wallacei X) is an evolutionary engine that allows users to run evolutionary simulations in Grasshopper 3D through utilising highly detailed analytic tools coupled with various comprehensive selection methods, including algorithmic clustering, to assist users to better understand their evolutionary runs, and make more informed decisions at all stages of their evolutionary simulations; including setting up the design problem, analysing the outputted results and selecting the desired solution or solutions for the final output. Wallacei also provides users with the ability to select, reconstruct and output any phenotype from the population after completing their simulation.
The free plugin is streamlined to give users efficient access to the data outputted by their evolutionary simulations, and enable clear and efficient methods for analysis and selection – The aim is for users (of all degrees of expertise) to better understand their evolutionary simulations, gain a thorough understanding of the outputted numeric values, and seamlessly extract the optimised data; all within one user interface.
Wallacei X employs the NSGA-2 algorithm (Deb et. al., 2001) as the primary evolutionary algorithm, and utilises the K-means method as the clustering algorithm. Additionally, Wallacei X incorporates the JMetal, LiveCharts and HelixToolkit libraries.
Wallacei is founded on the research conducted by Mohammed Makki during his Doctoral studies at the Architectural Association under the directorship of Dr. Michael Weinstock; and has been developed by Mohammed Makki, Milad Showkatbakhsh and Yutao Song.
Although Wallacei has been streamlined for Rhino 6, the plugin can also be installed in Rhino 5 (both 64 and 32 bit platforms).
To learn more about Wallacei, and gain access to video tutorials and the ‘Wallacei Primer’, please visit:
Hi, I made a simple example to learn how generative design with Wallacei works. I made a solid extruded from a square with a 1-20 slider for the height. I set a '0' value to all the solution up to 10…Continue
Started by Roberto Coppa. Last reply by Roberto Coppa Sep 1.
Hi,After running the simulation, the fitness values comes extremately high for two of the six objectives.Reinstating the genes for the selected solutions, those values come back to the expected…Continue
Started by Roig. Last reply by jansen.arch Aug 27.
Hi,I am running a bending active hybrid system optimization where i have 9 values as a genome input that control various heights. Fitness value is obtained after kangaroo zombie solver converges in…Continue
Started by Konstantinos Pagkalos. Last reply by Milad Showkatbakhsh Aug 26.