By optimizing the genetic algorithm's parameters (e.g., population size, crossover rate, mutation rate), we can improve the feature selection process in terms of both accuracy and efficiency. 6.2 ...

A group of researchers in the lab of Prof. Lucía Chávez Gutiérrez (VIB-KU Leuven) has unraveled the genetic contributions to ...

Small language models should be more cost effective to deploy than LLMs, offering greater privacy, and performing specific or ...

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear) ...

Library for Jacobian descent with PyTorch. It enables optimization of neural networks with multiple losses (e.g. multi-task learning).