应gg999策略手机白菜的邀请，法国国家信息与自动化研究所(INRIA)Dimo Brockhoff博士将于近期访问我院，并作学术报告。

        报告题目：Selected Current Research Topics in Evolutionary Multiobjective Optimization
        时    间：10月7日上午9：00-10：00
        地    点：理科楼122

        报告摘要：
        Multiobjective optimization problems occur in several application domains such as engineering design, finance, or optimal control and evolutionary multiobjective optimization (EMO) algorithms are the methods of choice if the problems are handled as a blackbox or when the objective functions are highly non-linear, rugged, noisy, etc.
        In this talk, I am going to present three examples of recent and current research areas in the active field of EMO which are highly linked to my own interests. First, I introduce the idea of optimal $\mu$-distributions as the goal in indicator-based EMO and state the most recent advances. Second, I mention some of our work on analyzing the expected runtime of simple stochastic search heuristics. Third, I will outline our recent efforts towards an automated benchmarking suite for comparing multiobjective blackbox algorithms (not necessarily EMO) based on the state-of-the-art benchmarking platform Coco which has been used in the single-objective blackbox optimization benchmarking (BBOB) workshops held at previous ACM-GECCO conferences.
        Besides giving a short overview of my own research interests, I will mention---wherever possible---interesting open research questions that may lead to first collaborative projects.

        报告人简介：
        Dimo Brockhoff received his diploma in computer science from University of Dortmund, Germany in 2005 and his PhD (Dr. sc. ETH) from ETH Zurich, Switzerland in 2009. Afterwards, he held postdoctoral research positions in France at INRIA Saclay Ile-de-France (2009-2010) and at Ecole Polytechnique (2010-2011). Since November 2011, he has been a permanent researcher at INRIA Lille - Nord Europe, France. His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on theoretical aspects of indicator-based search, algorithm design, and the benchmarking of (multiobjective) blackbox algorithms in general.  

        欢迎感兴趣的师生参加！