Neural Combinatorial Optimization: a New Player in the Field

Understand

Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of problems using Neural Network models and Reinforcement Learning.

• Recently, its good performance has encouraged many practitioners to develop neural architectures for a wide variety of combinatorial problems.
• However, the incorporation of such algorithms in the conventional optimization framework has raised many questions related to their performance and the experimental comparison with other methods such as exact algorithms, heuristics and metaheuristics.
• This paper presents a critical analysis on the incorporation of algorithms based on neural networks into the classical combinatorial optimization framework.