OptunaHub / Sampler

TPE Sampler

Sampler using TPE (Tree-structured Parzen Estimator) algorithm.

Simulated Annealing Sampler

Sampler based on simulated annealing algorithm.

Simple Sampler

An easy sampler base class to implement custom samplers.

Sampler using Whale Optimization Algorithm

Swarm Algorithm Inspired by Pod of Whale

Random Search

Sampler using random sampling.

QMC Search

Sampler using Quasi Monte Carlo sampling.

PyCMA Sampler

A CMA-ES Sampler using cma library as the backend.

PFNs4BO sampler

In-context learning for Bayesian optimization. This sampler uses Prior-data Fitted Networks (PFNs) as a surrogate model for Bayesian optimization.

Partial Fixed Sampler

Sampler with partially fixed parameters.

NSGAIII Search

Sampler using NSGAIII algotithm.

NSGAII Search

Sampler using NSGAII algotithm.

Implicit Natural Gradient Sampler

A sampler based on Implicit Natural Gradient.

HEBO (Heteroscedastic and Evolutionary Bayesian Optimisation)

HEBO addresses the problem of noisy and heterogeneous objective functions by using a heteroscedastic Gaussian process and an evolutionary algorithm.

Grid Search

Sampler using grid search.

Gaussian Process-Based Sampler

Sampler using Gaussian process-based Bayesian optimization.

Evolutionary LLM Merge Sampler

A sampler for evolutionary LLM merge.

Demo Sampler

Demo Sampler of OptunaHub

CMA-ES Sampler

A sampler using cmaes as the backend.

Brute Force Search

Sampler using brute force.

BoTorch Sampler

A Sampler using botorch library as the backend.