“Machine learning is normally very data intensive, and it’s difficult to generate a lot of data when you’re using high-quality data from finite element analysis. But the multi-objective Bayesian ...

OLA2024 welcomes presentations that cover any aspects of optimization and learning research such as new high-impact applications, parameter tuning, 4th industrial revolution, new research challenges, ...

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text ...

Tan Liu, Qinyun Yuan, Lina Wang, Yonggang Wang, Nannan Zhang, Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved ...