Abstract
The cutting-edge evolutionary computation algorithm CatCMA has been published on OptunaHub. CatCMA is an algorithm that excels in mixed search spaces with continuous and discrete variables.
This figure is from https://arxiv.org/abs/2405.09962.
📝 Introduction to CatCMA in OptunaHub: Blog post by Hideaki Imamura.
Class or Function Names
- CatCmaSampler
Installation
pip install -r https://hub.optuna.org/samplers/catcma/requirements.txt
Example
import numpy as np
import optuna
from optuna.distributions import CategoricalDistribution
from optuna.distributions import FloatDistribution
import optunahub
def objective(trial: optuna.Trial) -> float:
x1 = trial.suggest_float("x1", -1, 1)
x2 = trial.suggest_float("x2", -1, 1)
x3 = trial.suggest_float("x3", -1, 1)
X = np.array([x1, x2, x3])
c1 = trial.suggest_categorical("c1", [0, 1, 2])
c2 = trial.suggest_categorical("c2", [0, 1, 2])
c3 = trial.suggest_categorical("c3", [0, 1, 2])
C = np.array([c1, c2, c3])
return sum(X**2) + len(C) - sum(C == 0)
if __name__ == "__main__":
mod = optunahub.load_module(
package="samplers/catcma",
)
CatCmaSampler = mod.CatCmaSampler
study = optuna.create_study(
sampler=CatCmaSampler(
search_space={
"x1": FloatDistribution(-1, 1),
"x2": FloatDistribution(-1, 1),
"x3": FloatDistribution(-1, 1),
"c1": CategoricalDistribution([0, 1, 2]),
"c2": CategoricalDistribution([0, 1, 2]),
"c3": CategoricalDistribution([0, 1, 2]),
}
)
)
study.optimize(objective, n_trials=20)
print(study.best_params)
# You can omit the search space definition before optimization.
# Then, the search space will be estimated during the first trial.
# In this case, independent_sampler (default: RandomSampler) will be used instead of the CatCma algorithm for the first trial.
study = optuna.create_study(sampler=CatCmaSampler())
study.optimize(objective, n_trials=20)
print(study.best_params)
Others
Reference
Ryoki Hamano, Shota Saito, Masahiro Nomura, Kento Uchida, Shinichi Shirakawa , CatCMA : Stochastic Optimization for Mixed-Category Problems, GECCO'24
See the arXiv paper or ACM paper for more details.
BibTeX
@article{hamano2024catcma,
title={CatCMA: Stochastic Optimization for Mixed-Category Problems},
author={Hamano, Ryoki and Saito, Shota and Nomura, Masahiro and Uchida, Kento and Shirakawa, Shinichi},
journal={arXiv preprint arXiv:2405.09962},
year={2024}
}
- Package
- samplers/catcma
- Author
- Masahiro Nomura
- License
- MIT License
- Verified Optuna version
- 3.6.1
- Last update
- 2024-08-27