报告内容：Data driven evolutionary optimisation of complex systems
报告人:金耀初 教授
报告时间：12月18日 19:00
报告方式：线上（腾讯会议：848 590 447）
指导人简介:
金耀初(Yaochu Jin)目前为英国萨里大学“计算智能”首席教授，IEEE Fellow。曾任中国教育部“长江学者奖励计划”讲座教授，芬兰国家创新局“Finland Distinguished Professor”，IEEE计算智能学会副主席（2014-2015）。目前是IEEE Transactions on Cognitive and Developmental Systems 主编，Complex & Intelligent Systems 共同主编，IEEE 杰出演讲人（2013-2015，2017-2019）。在进化算法、机器学习等领域方面发表论文300余篇，Google Scholar引用15000余次，先后在近30个国际会议上作特邀大会或主题报告。荣获2017年度“IEEE进化计算汇刊优秀论文奖”，2014、2016年度“IEEE 计算智能杂志优秀论文奖”，“2017年世界进化计算大会最佳学生论文奖”以及“2014年计算智能理论国际研讨会最佳学生论文奖”。他指导的博士学位论文获“2018年度IEEE计算智能学会优秀博士论文奖”。研究方向涉及人工智能的多个领域，包括进化计算，多目标优化与决策，大数据、稀疏数据驱动的进化优化，多目标机器学习、安全机器学习，分布式机器学习等及其在复杂工业过程、健康医疗及群机器人等方面的应用。
报告内容简介：
Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. In-stead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for per-forming optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.
