報告內容🎫：Data driven evolutionary optimisation of complex systems
報告人:金耀初 教授
報告時間：12月18日 19:00
報告方式：線上（騰訊會議📳：848 590 447）
指導人簡介:
金耀初(Yaochu Jin)目前為英國薩裏大學“計算智能”首席教授😆，IEEE Fellow。曾任中國教育部“長江學者獎勵計劃”講座教授🙇♀️，芬蘭國家創新局“Finland Distinguished Professor”，IEEE計算智能學會副主席（2014-2015）。目前是IEEE Transactions on Cognitive and Developmental Systems 主編👮🏽♂️，Complex & Intelligent Systems 共同主編🤸♂️，IEEE 傑出演講人（2013-2015，2017-2019）。在進化算法、機器學習等領域方面發表論文300余篇，Google Scholar引用15000余次🦸🏼♂️，先後在近30個國際會議上作特邀大會或主題報告🐦🔥。榮獲2017年度“IEEE進化計算匯刊優秀論文獎”🦹，2014🫲🏽🤵🏽、2016年度“IEEE 計算智能雜誌優秀論文獎”🌌↙️，“2017年世界進化計算大會最佳學生論文獎”以及“2014年計算智能理論國際研討會最佳學生論文獎”。他指導的博士學位論文獲“2018年度IEEE計算智能學會優秀博士論文獎”。研究方向涉及人工智能的多個領域🤤，包括進化計算🏰，多目標優化與決策♛，大數據、稀疏數據驅動的進化優化，多目標機器學習、安全機器學習🧑🏻🦽，分布式機器學習等及其在復雜工業過程、健康醫療及群機器人等方面的應用😟。
報告內容簡介：
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.
