Scalability of optimization algorithms is a major challenge in co** with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …

Metaheuristic algorithms are extensively recognized as effective approaches for solving high-
dimensional optimization problems. These algorithms provide effective tools with important …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

Identification of variable interaction is essential for an efficient implementation of a divide-
and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an …

Cooperative co-evolution (CC) is an evolutionary computation framework that can be used
to solve high-dimensional optimization problems via a “divide-and-conquer” mechanism …

The world continues to generate quintillion bytes of data daily, leading to the pressing needs
for new efforts in dealing with the grand challenges brought by Big Data. Today, there is a …

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high
diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for …

This paper proposes an evolution strategy (ES) algorithm called hybrid sampling-evolution
strategy (HS-ES) that combines the covariance matrix adaptation-evolution strategy (CMA …

Large-scale optimization with high dimensionality and high computational cost becomes
ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed …