137 citations · 351 across the 41 of their papers we have counts for
4 papers · 1 filter
Half a Dozen Real-World Applications of Evolutionary Multitasking, and More
Abhishek Gupta, Lei Zhou, Yew-Soon Ong +2
Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of e…
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
Jian Cheng Wong, Chinchun Ooi, Abhishek Gupta +1
A physics-informed neural network (PINN) uses physics-augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensur…
Multi-Party Dual Learning
Maoguo Gong, Yuan Gao, Yu Xie +3
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed partie…
Multi-Space Evolutionary Search for Large-Scale Optimization
Liang Feng, Qingxia Shang, Yaqing Hou +2
In recent years, to improve the evolutionary algorithms used to solve optimization problems involving a large number of decision variables, many attempts have been made to simplify…