“Exploration and exploitation are the two cornerstones of problem solving by search.” For
more than a decade, Eiben and Schippers' advocacy for balancing between these two …

Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

Hyperparameter tuning is a critical function necessary for the effective deployment of most
machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of …

This paper provides a systematic review of the Genetic Algorithm (GA) s proposed to solve
planning and scheduling problems in Engineer-To-Order (ETO) contexts. Our review …

With the pollution caused by express packaging waste, eco-packages are being commonly
used to promote a green supply chain. However, extant research has yet to address the …

Cost and physical constraints in the engineering applied problems obligate finding the best
results that global optimization algorithms cannot realize. For accurate and faster …

Bayesian optimization (BO) is a popular global optimization scheme for sample-efficient
optimization in domains with expensive function evaluations. The existing BO techniques …

Customer segmentation refers to dividing customer groups into multiple different sub-
communities according to customer characteristics. The accurate segmentation of customers …

Most of the traditional histogram-based thresholding techniques are effective for bi-level
thresholding and unable to consider spatial contextual information of the image for selecting …