Prof. Yaochu Jin

Graph Neural Networks for Combinatorial Optimization

Abstract: Graph neural networks have been found successful in solving combinatorial optimization problems. This talk starts with a simple example of solving the travelling salesman problem using graph neural networks. Then, we present an approach to multi-objective facility location using two graph neural networks with supervised training. Finally, we showcase how a graph neural network with negative message passing can be trained using unsupervised training for solving graph coloring problems. We conclude the talk with a summary and discussion of future work.

Bio-Sketch: Yaochu Jin is an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, with the Faculty of Technology, Bielefeld University, Germany. He is also a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor”of University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include multi-objective and data-driven evolutionary optimization, evolutionary multi-objective learning, trustworthy AI, and evolutionary developmental AI.

Prof Jin is presently the President-Elect of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He was named by the Web of Science as“a Highly Cited Researcher”from 2019 to 2022 consecutively. He is a Member of Academia Europaea and Fellow of IEEE.