Abstract This package automatically selects an appropriate sampler for the provided search space based on the developers’ recommendation. The following article provides detailed information about AutoSampler.
📰 AutoSampler: Automatic Selection of Optimization Algorithms in Optuna Class or Function Names AutoSampler This sampler currently accepts only seed and constraints_func. constraints_func enables users to handle constraints along with the objective function. These arguments follow the same convention as the other samplers, so please take a look at the reference.
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.