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The next-gen Grasshopper optimization tool.

Settings Tab

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Allows detailed optimization settings to be performed in the UI.
See below for the meaning of each setting.

Useful resources for understanding each algorithm settings

TPE (Tree-structured Parzen Estimator)

TPE fits one Gaussian Mixture Model (GMM) l(x)to the set of parameter values associated with the best objective values, and another GMM g(x)to the remaining parameter values. It chooses the parameter value xthat maximizes the ratio l(x)/g(x).

API Reference

Referenced Papers

About constraints

How many should the Number of startup trials be?

  • Bayesian optimization first performs random sampling to create a surrogate model. This is the setting for how many trials random sampling is performed.
  • The original paper recommends “number of variables” * 11-1.

BoTorch (Gaussian Process)

API Reference

About constrains

  • A value strictly larger than 0 means that a constraints is violated. A value equal to or smaller than 0 is considered feasible.
  • more detail Constraints · BoTorch

CMA-ES (Covariance Matrix Adaptation Evolution Strategy)

API Reference

NSGA-II (Non-dominated Sorting Genetic Algorithm)

Multi-objective sampler using the NSGA-II algorithm.

API Reference

Referenced Paper

What difference of corossover algorithm

About constraints

QMC (Quasi-Monte Carlo)

A Quasi Monte Carlo Sampler that generates low-discrepancy sequences.

API Reference

This process uses scipy inside optuna. If you want to know the detailed process, please refer to the following scipy documentation.

When should QMC be selected instead of random?

Random sampling, as the name implies, samples randomly. This means that the spacing between the sampled points is not guaranteed to be far apart, and it is possible to sample a point right next to a point that has already been sampled.
On the other hand, quasi-Monte Carlo allows for random sampling with some spacing. For example, if you want to check the entire solution space with a small sample, you may be able to do so faster than with random.