Biomorpher

162 members

Description

Interactive Evolutionary Algorithms for Rhino Grasshopper.

As opposed to setting objective functions (As with Galapagos for example), Interactive Evolutionary Algorithms (IEAs) allow designers to engage with the process of evolutionary development itself. This creates an involved experience, helping to explore the wide combinatorial space of parametric models without always knowing where you are headed.

See github site for source (MIT) and latest release:

https://github.com/johnharding/Biomorpher/releases

Cecilie Brandt Olsen (author of K2 Engineering) and I have been developing Biomorpher since December 2016.

This work is sponsored by the 2016/17 UWE VC Early Career Researcher Development Award and was initially inspired by Richard Dawkins' Biomorphs from his 1986 book, The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe without Design.

Please leave comments and share your experience of using Biomorpher below. It would be great to hear from you!

Comments Discussions Members

Option for Running multiple generations

by Mihir Vasani

Nov 29, 2017

Hi John, Thanks a lot for such awesome plugin !!! Great user interactivity and interface which lacked in octopus/Galapagos. It would be helpful if u can add an option for multiple generations and convergence graph. Additionally marking the fittest of each criteria in design and history panel with its respective color code would be great.

  • up

    John Harding

    Hi Mihir,

    Thanks for your kind words and feedback. I've now integrated multiple generations and will be in the next release, should be very soon!

    Convergence graph has been on the cards and working on that one. Good to hear it will be of use though.

    In terms of colours for identifying best of a population in the history tree, that's a great idea- thanks! I will definitely implement.

    Actually, on the history tree I've been wondering whether to show all 12 cluster centroids or not for artificial selection as well, but not sure... what do you think? Or else for both artificial and performance only show the optimal in the history tree? Anyway, do let me know and thanks again.

    Best wishes,

    John

    1

    Dec 9, 2017

    • up

      Michael Dubby

      When working with Color Measuring Devices, running multiple generations of measurements can be essential for achieving consistent and accurate results. Multiple generations refer to repeated testing cycles to refine calibration, validate accuracy, and ensure repeatable performance under various conditions. This approach is particularly valuable in industries like printing, textiles, and manufacturing, where color precision is critical. By running several generations, users can identify variations, improve device settings, and maintain long-term quality control. Modern color measurement technologies make it easier to manage and compare multiple data sets, ensuring every generation builds toward greater reliability and consistency in results.

      Aug 11