Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...

A hybrid geothermal–biomass system integrates multigeneration heating, cooling, and power through thermodynamic and economic optimization. Using exergy analysis and particle swarm optimization, the ...

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...

1 Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou, China 2 NARI Technology Co., Ltd., Nanjing, China The stability and economic dispatch efficiency of ...

Abstract: The Dynamic Resource Allocation Multi-Objective Optimization Algorithm (MOEA/D-DRA) is a method for solving multi-objective optimization problems (MOPs). This algorithm enhances the ...

Implementation of hypernetwork augmented Actor-Critic in multi-objective reinforcement learning for MsC Thesis: "Generalizing Pareto optimal policies in multi-objective reinforcement learning" ...

Change is the only constant in today’s rapidly evolving digital marketing landscape. Keeping up with the latest innovations isn’t just a choice – it’s a necessity for survival. Generative engine ...

Abstract: Sequences of linear systems arise in the predictor-corrector method when computing the Pareto front for multi-objective optimization. Rather than discarding information generated when ...

This repository contains Evolutionary Algorithms that can be used for multi-objective optimization. Interactive optimization is supported. Methods such as RVEA and NSGA-III can be found here.