Many traditional algorithms for solving combinatorial optimization problems involve using
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …

Portfolio optimization has always been a challenging proposition in finance and
management. Portfolio optimization facilitates in selection of portfolios in a volatile market …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving
challenging combinatorial optimization problems without specialized algorithm design by …

The rise of the new generation of cyber threats demands more sophisticated and intelligent
cyber defense solutions equipped with autonomous agents capable of learning to make …

Routing problems are a class of combinatorial problems with many practical applications.
Recently, end-to-end deep learning methods have been proposed to learn approximate …

Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …

Combinatorial optimization algorithms for graph problems are usually designed afresh for
each new problem with careful attention by an expert to the problem structure. In this work …