IEEE

Dynamic Heuristic Optimization in High-Order Runge–Kutta Schemes Using Reinforcement Learning and Genetic Algorithms

Abstract: This paper introduces a hybrid optimisation framework that integrates Genetic Algorithms (GAs) and Reinforcement Learning (RL) for the construction of high-order Runge–Kutta (RK) schemes.
IEEE

Enhancing Clustering Performance With Tensorized High-Order Bipartite Graphs: A Structured Graph Learning Approach

Abstract: Clustering based on structured graph learning involves acquiring a proximity matrix with an explicit clustering structure from the original one. However, the original proximity matrix often ...
Frontiers

GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference

Gene regulatory network (GRN) inference is a central task in systems biology. However, due to the noisy nature of gene expression data and the diversity of regulatory structures, accurate GRN ...