The divergence of ASIC designs makes it difficult to run commonly used modern sequencing analysis pipelines due to software ...

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 ...

Genetic Programming ... and reduce genetic programming hyper-heuristics (GPHH) search space. However, more research is needed to quantify the contribution of features in the GPHH to many-objective JSS ...

Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the "light ...

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 ...

This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based ...

This paper introduces Genetic Programming for Explainable Manifold Learning (GP-EMaL), a novel integration of Genetic Programming (GP) and Explainable Artificial Intelligence (XAI). GP-EMaL leverages ...