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Minh Tran, Duc Pham-Hi and Marc Bui
In this paper, we propose a novel approach to optimize parameters for strategies in automated trading systems. Based on the framework of Reinforcement learning, our work includes the development of a learning environment, state representation, reward fun...
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Pavlos Nikolaidis
Renewable energy sources are constantly increasing in the modern power systems. Due to their intermittent and uncertain potential, increased spinning reserve requirements are needed to conserve the reliability. On the other hand, each action towards effi...
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Wenyu Cao, Benbo Sun and Pengxiao Wang
Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the applicat...
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Maxime Gobert, Jan Gmys, Jean-François Toubeau, Nouredine Melab, Daniel Tuyttens and François Vallée
Bayesian Optimization (BO) with Gaussian process regression is a popular framework for the optimization of time-consuming cost functions. However, the joint exploitation of BO and parallel processing capabilities remains challenging, despite intense rese...
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Marvin Kastner, Nicole Nellen, Anne Schwientek and Carlos Jahn
At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, ma...
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