Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Large language models (LLMs), designed to provide helpful and safe responses, often rely
on alignment techniques to align with user intent and social guidelines. Unfortunately, this …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …

In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …

Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …

In the past decade, there has been an increasing number of computational screening works
to facilitate finding optimal materials for a variety of different applications. Unfortunately, most …

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for
solving expensive optimization problems where only a small number of real fitness …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …