Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built
using accounting-based financial ratios. This study proposes a bankruptcy prediction model …

Previous researchers have made impressive strides in develo** algorithms and solution
methodologies to address multi-objective optimization (MOO) problems in industrial …

A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive
industry creates a high added value for this business network. This fact motivates the …

In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …

In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …

Abstract The 2030 Sustainable Development Goals (SDGs) aim at jointly improving
economic, social, and environmental outcomes for human prosperity and planetary health …

With increasing environmental awareness and energy requirement, sustainable
manufacturing has attracted growing attention. Meanwhile, distributed manufacturing …

Hypervolume is widely used as a performance indicator in the field of evolutionary
multiobjective optimization (EMO). It is used not only for performance evaluation of EMO …