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Andreas Maniatopoulos, Paraskevi Alvanaki and Nikolaos Mitianoudis
The recent boom of artificial Neural Networks (NN) has shown that NN can provide viable solutions to a variety of problems. However, their complexity and the lack of efficient interpretation of NN architectures (commonly considered black box techniques) ...
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Adel Younis and Zuomin Dong
The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As...
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Giulia Pugliese, Xiaochen Chou, Dominic Loske, Matthias Klumpp and Roberto Montemanni
Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, a...
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Raz Lapid, Zvika Haramaty and Moshe Sipper
Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating advers...
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Mario Andrés Muñoz and Michael Kirley
In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization. We construct a machine learning-based algorithm selector, which u...
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