Grasshopper
Octopus
Octopus is a plug-in for applying evolutionary principles to parametric design and problem solving. It allows the search for many goals at once, producing a range of optimized trade-off solutions between the extremes of each goal.
Also see octopus.E for custom evolutionary algorithms.
Download the latest version on food4rhino
It is part of a range of tools developed at the University of Applied Arts Vienna, and Bollinger+Grohmann Engineers.
- search for single goal + diversity of solutions
- search for best trade offs between 2 to X goals
- improve solutions by similarity-goals
- choose preferred solutions during a search
- change objectives during a search
- solutions' 3d models for visual feedback
- recorded history
- save all search data within the Grasshopper document
- save a solution as a Grasshopper State
- export to text or text files
Octopus introduces multiple fitness values to the optimization. The best trade-offs between those objectives are searched, producing a set of possible optimum solutions that ideally reach from one extreme trade-off to the other.
Based on SPEA-2 and HypE from ETH Zürich and David Rutten's Galapagos User Interface. Developed by Robert Vierlinger in cooperation with Christoph Zimmel, karamba3d.com and Bollinger+Grohmann Engineers.
To install:
- Copy the .gha and .dll file into the Grasshopper components folder
- Right-click the file > Properties > make sure there is no "blocked" text
- Restart Rhino and Grasshopper
Some examples are provided here.
New commented examples and a brief manual are provided in the download of octopus on food4rhino.
Running octopus iteration every time quelea agent's lifetime has finished
Ok so I have made this 5 parametric lofts (pic 1). In this case only 2 of them work has environment for quelea agents to move on them (pic 2).
The agents generated by this agent-base component Quelea (http://www.food4rhino.com/app/quelea-agent-based-design-grasshopper) take like 40 seconds when the toggle activates to go from one end of the ramp to another.
With proximity 3d i'm analyzing each instance the agents are closer than x units. In picture 3 we can see that in 212 instances the agent are closer than those x units.
Finally all the genes that controll the ramps are connected to the G of octopus component and one of the conflicting objectives connected to the O of octopus component is the number of instance quelea agents get close.
So the thing I need is to iterate the ramps controling the genes with octopus but activating the boolean toggle (quelea run) each time the ramps are modified so the agents take 40 seconds to perambulate the environment, analyze the instance they get close and let octopus iterate again searching for a optimized environment.
Aditya Tognatta
you could count the number of iteration you want before any calculations needs to be done on them. then create a stream filter that passes the data through it.
Hope this helps!
Nov 10, 2016
Andrés Utz
maybe yo can activate quelea with a cull pattern that set true only when new geometry been created.
As weel you can cull only analyzed geometry with quelea as valid solution in octopus.
Nov 10, 2016
Michael Fluer
Hola, un amigo me recomendó probar algo diferente para desconectarme después de un día largo y encontré https://baxter-bet.es . Aproveché los bonos exclusivos para jugadores de España y empecé con uno de sus juegos de tragamonedas; al principio no gané nada, pero después de unas rondas más arriesgadas conseguí un premio que me sorprendió. Me gustó lo fácil que es navegar por todo y cómo los bonos añaden un extra de emoción. Ahora es mi manera favorita de relajarme un rato y distraerme un poco.
yesterday