简介：

詹志辉，南开大学人工智能学院教授，博士生导师，IEEE Fellow、IEEE计算智能学会杰出青年奖获得者（每年全球遴选一位）、吴文俊人工智能优秀青年奖获得者、科睿唯安“全球高被引科学家”、人工智能领域全球前2%顶尖科学家（同时入选年度科学影响力和终身科学影响力双榜单）、2014-2025连续12年中国高被引学者。主要研究领域包括人工智能、进化计算和群体智能及其应用，担任进化计算、人工智能和控制领域顶尖国际学术期刊 IEEE Transactions on Evolutionary Computation、IEEE Transactions on Artificial Intelligence、IEEE Transactions on Emerging Topics in Computational Intelligence和IEEE Transactions on Systems, Man, and Cybernetics: Systems等4本IEEE Transactions期刊的副主编。

时间：2026年9月5日  9:00-10:00

地点：H203

摘要：

Evolutionary computation (EC) is a kind of powerful artificial intelligence (AI) method for optimization. The EC simulates the evolutionary phenomenon and swarm intelligent behavior 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) and knowledge-driven EC (KDEC) have become popular in helping EC algorithms solve these challenging optimization problems. This talk will focus on what to drive in DDEC/KDEC and how to drive the DDEC/KDEC. For what to drive, we focus on building surrogate for fitness evaluation to drive selection and focus on learning successful pattern for help generating solutions to drive evolution. Then in data-driven for selection, we talk about Boosting Data-Driven Evolutionary Algorithm (BDDEA) and Hierarchical and Ensemble Surrogate-assisted Evolutionary Algorithm (HES-EA); in data-driven for evolution, we talk about Learning-aided Evolution for Optimization (LEO) and Knowledge Learning for Evolutionary Computation (KLEC). 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.