Based on the above motivation, this paper proposes a decision variables classification approach ... is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms ...

Abstract: Regression has been successfully combined with the memetic algorithm (MA) for constructing surrogate models. It is essentially an attempt to approximate the objective or constraint landscape ...

c-lasso is a Python package that enables sparse and robust linear regression and classification with linear ... including path and proximal splitting algorithms, are implemented to solve the ...

If you have used our code for research purposes, please cite the publications mentioned above. For the sake of simplicity, we provide the Bibtex format: @article{Lapucci2024, author={Lapucci, Matteo ...

In Talbi (2021), the authors propose a unified method for describing various optimization algorithms that ... for a variety of image classification tasks. Table 1 shows the main results of the μNAS ...

Researchers from University of Tsukuba have presented counterexamples to assumed key properties of Asynchronous Distributed OPTimization (ADOPT) and its successor algorithms. ADOPT is a well-known ...

Classification ... performance of the algorithm on a given dataset. There are different methods for hyperparameter tuning, such as grid search, random search, or Bayesian optimization, that ...