Aqua QA Optimized using HyperTuning

Automated hyperparameter tuning in Julia. HyperTuning aims to be intuitive, capable of handling multiple problem instances, and providing easy parallelization.


This package can be installed on Julia v1.7 and above. Use one of the following options.

Via Pkg module:

julia> import Pkg; Pkg.add("HyperTuning")

Via the Julia package manager, type ] and

pkg> add HyperTuning

Quick Start

Let's begin using HyperTuning to optimize $f(x,y)=(1-x)^2+(y-1)^2$. After that, the hyperparameters and budget are given in a new scenario. Once the scenario and the objective function are defined, the optimization process begins.

julia> using HyperTuning

julia> function objective(trial)
           @unpack x, y = trial
           (1 - x)^2 + (y - 1)^2
objective (generic function with 1 method)

julia> scenario = Scenario(x = (-10.0..10.0),
                           y = (-10.0..10.0),
                           max_trials = 200);

julia> HyperTuning.optimize(objective, scenario)
Scenario: evaluated 200 trials.
          parameters: x, y
   space cardinality: Huge!
           instances: 1
          batch_size: 8
             sampler: BCAPSampler{Random.Xoshiro}
              pruner: NeverPrune
          max_trials: 200
           max_evals: 200
         stop_reason: HyperTuning.BudgetExceeded("Due to max_trials")
│     Trial │      Value │
│       198 │            │
│         x │   0.996266 │
│         y │    1.00086 │
│    Pruned │      false │
│   Success │      false │
│ Objective │ 1.46779e-5 │

julia> @unpack x, y = scenario


  • Intuitive usage: Define easily the objective function, the hyperparameters, start optimization, and nothing more.
  • Muti-instance: Find the best hyperparameters, not for a single application but multiple problem instances, datasets, etc.
  • Parallelization: Don't worry, simply start julia -t8 if you have 8 available threads or julia -p4 if you want 4 distributed processes, and the HyperTuning does the rest.
  • Parameters: This package is compatible with integer, float, boolean, and categorical parameters; however permutations and vectors of numerical values are compatible.
  • Samplers: BCAPSampler for a heuristic search, GridSampler for brute force, and RandomSampler for an unbiased search.
  • Pruner: NeverPrune to never prune a trial and MedianPruner for early-stopping the algorithm being configured.


Please, cite us if you use this package in your research work.

Mejía-de-Dios, JA., Mezura-Montes, E. & Quiroz-Castellanos, M. Automated parameter tuning as a bilevel optimization problem solved by a surrogate-assisted population-based approach. Appl Intell 51, 5978–6000 (2021). https://doi.org/10.1007/s10489-020-02151-y

  author = {Jes{\'{u}}s-Adolfo Mej{\'{\i}}a-de-Dios and Efr{\'{e}}n Mezura-Montes and Marcela Quiroz-Castellanos},
  title = {Automated parameter tuning as a bilevel optimization problem solved by a surrogate-assisted population-based approach},
  journal = {Applied Intelligence},
  doi = {10.1007/s10489-020-02151-y},
  url = {https://doi.org/10.1007/s10489-020-02151-y},
  year = {2021},
  publisher = {Springer Science and Business Media {LLC}},
  volume = {51},
  number = {8},
  pages = {5978--6000}


Add the following in your Markdown docstring:

[![Optimized using HyperTuning](https://raw.githubusercontent.com/jmejia8/HyperTuning.jl/main/badge.svg)](https://github.com/jmejia8/HyperTuning.jl)

to show the badge Optimized using HyperTuning


To start contributing to the codebase, consider opening an issue describing the possible changes. PRs fixing typos or grammar issues are always welcome.