Tutorial

Tutorial II

Data-Driven Evolutionary Computation: What to Drive and How to Drive?

Zhihui Zhan
Nankai University, Tianjin, China

Evolutionary computation (EC) is a kind of powerful artificial intelligence (AI) method for optimization. The EC simulates the evolutionary phenomenon and swarm intelligent behaviours in nature, being promising in knowledge creation and problem solving. As the EC algorithms follow the Darwin's “survival of the fittest” principle to select better solutions and to reproduce new solutions, they may face difficulties when deal with expensive optimization problem if the fitness evaluation is very time/cost consuming or even the fitness function cannot be formulated. The complex optimization problems also challenge the EC algorithms to make them easy to be trapped into local optima or to take too long time to converge to the promising region. Therefore, data-driven EC (DDEC) has become popular in helping EC algorithms solve these challenging optimization problems. This talk will focus on what to drive in DDEC and how to drive the DDEC. For what to drive, we focus on building a surrogate for fitness evaluation to drive selection and focus on learning successful patterns to help generate solutions to drive evolution. Then, in data-driven selection, we talk about Boosting Data-Driven Evolutionary Algorithm and Hierarchical and Ensemble Surrogate-assisted Evolutionary Algorithm; in data-driven evolution, we talk about Learning-aided Evolution for Optimization and Knowledge Learning for Evolutionary Computation. We hope such new EC paradigms can provide new ways for solving modern ultra-complex optimization problems and promote the new developments of EC and AI.

Biosketch

Zhihui Zhan is currently a Changjiang Scholar Young Professor and Gifted Professor at Nankai University, Tianjin, China. Prof. Zhan is an IEEE Fellow. He was a recipient of the IEEE Computational Intelligence Society Outstanding Early Career Award in 2021, the Outstanding Youth Science Foundation from the National Natural Science Foundation of China in 2018, and the Wu Wen-Jun Artificial Intelligence Excellent Youth from the Chinese Association for Artificial Intelligence in 2017. He is listed as a Highly Cited Researcher by Clarivate Analytics, as the World’s Top 2% Scientist for both Career-Long Impact and Year Impact in AI, and as the Elsevier Highly Cited Chinese Researcher in Computer Science from 2014 to 2024. He is currently an Associate Editor of the IEEE Trans. on Artificial Intelligence, IEEE Trans. on Evolutionary Computation, IEEE Trans. on Emerging Topics in Computational Intelligence, and IEEE Trans. on Systems, Man and Cybernetics: Systems.