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.
Abstract Differential Evolution (DE) Sampler This implementation introduces a novel Differential Evolution (DE) sampler, tailored to optimize both numerical and categorical hyperparameters effectively. The DE sampler integrates a hybrid approach:
Differential Evolution for Numerical Parameters: Exploiting DE’s strengths, the sampler efficiently explores numerical parameter spaces through mutation, crossover, and selection mechanisms. Random Sampling for Categorical Parameters: For categorical variables, the sampler employs random sampling, ensuring comprehensive coverage of discrete spaces. The sampler also supports dynamic search spaces, enabling seamless adaptation to varying parameter dimensions during optimization.