Run all possible Generation

How can I run all possible genes combination and sort out best solutions from those ???

Sometimes we stuck on local values and mutations doesn’t help a lot at those moment.

Galapagos? Sorry if I misinterpreted your question if by ‘genes’ you mean something other than sliders/values.

No basically It’s sliders.

For example I have 3 sliders & all possible combinations created by those sliders are 1 million so is it possible to run all of 1 million solutions and then sort out best 1,000 solutions from those?

You can use the Brute Force solver from TT Toolbox:

1 Like

Thank you Anders but it’s not recommending because of too many permutations so what is the other option?

Is there any optimizer which can run verious permutations from all results instead of going for a locally optimized value?

This is exactly what the TT Toolbox Brute Force solver does!

If you’re looking for generic solvers that can explore fitness landscapes using “smarter” heuristics, you can use the native Galapagos solver, or one of the many third party options (e.g. Octopus and Goat).

1 Like

I tried Galapagos, can you tell me one thing what is the difference between Octopus and Wallacei??

I don’t know anything about Wallacei. But I do know that Octopus implements multi-objective algorithms, whereas Galapagos “only” has single-objective. You can ask about Wallacei over here:

1 Like

Thank you… have a great day.

1 Like

Octopus:

  • Focus: Octopus is primarily focused on multi-objective optimization. It is used when you have multiple design objectives or criteria that you want to optimize simultaneously. These objectives can be conflicting, and Octopus helps find a set of solutions that represent trade-offs between them.
  • Multi-Objective: Octopus allows you to evaluate and optimize designs based on multiple performance metrics, such as energy efficiency, structural stability, and daylighting, among others.
  • Generational Algorithm: Octopus typically uses a generational evolutionary algorithm to search for the best solutions over multiple generations of design variations.

Wallacei:

  • Focus: Wallacei, on the other hand, is primarily focused on performing generational evolutionary simulations and exploring the design space. It is not limited to optimization but is more about understanding the variability and performance of design solutions.
  • Performance Variability: Wallacei allows you to explore how design parameters impact the performance of your designs across a range of simulation runs. This can help you gain insights into the design space and identify regions of interest.
  • Visualization and Analysis: Wallacei provides tools for visualizing the distribution of design solutions and their performance across multiple simulations.
2 Likes

That process can be called optioneering. Here is the original interface to navigate all possible solutions: Design Explorer | Thornton Tomasetti

1 Like