Adaptability of neural networks for combinatorial optimization

摘要

In this work, we exploit adaptability neural networks properties for solving a series of instances sufficiently closed each other in combinatorial optimization fields. We show that using relevant information from a previous solved instance we obtain high quality solutions within shorter computational time in comparison to a standard neural network resolution.