Data-efficient image classification, which focuses on achieving accurate classification performance with limited labeled data, has garnered significant attention. Genetic programming (GP) has achieved impressive progress in image classification, particularly in scenarios involving small amounts of labeled data. GP research typically focuses on designing tree-based model representations to learn useful

Plenty of decision variable grouping based algorithms have shown satisfactory performance in solving high-dimensional optimization problems. However, most of them are tailored for inexpensive optimization problems. Extending variable grouping method to expensive optimization problems poses many challenges. One of the greatest challenges is that most grouping approaches require additional function evaluations

Efficient truck dispatching is crucial for optimizing container terminal operations within dynamic and complex scenarios. Despite good progress being made recently with more advanced uncertainty-handling techniques, existing approaches still have generalization issues and require considerable expertise and manual interventions in algorithm design. In this work, we present deep reinforcement learning-assisted

Distributed optimization has attracted lots of attention in recent years. Thanks to the intrinsic parallelism and great search capacity, evolutionary computation (EC) has the potential for black-box and non-convex distributed optimization. However, due to the decentralization of local objective functions, it is challenging to optimize the global objective function with efficient communication and guaranteed