Evolutionary Many-Objective Optimization: Algorithms, Test Problems and Performance Indicators

You are cordially invited to the upcoming technical talk:

Evolutionary Many-Objective Optimization: Algorithms, Test Problems and Performance Indicators

Speaker: Hisao Ishibuchi, Professor, Graduate School of Engineering, Osaka Prefecture University and IEEE Computational Intelligence Society Distinguished Lecturer

Co-organized by: IEEE CI/SMC Ottawa Joint Chapter (http://www.ieeeottawa.ca/ci), Instrumentation & Measurement (IMS) chapter and University of Ottawa Computer Science Graduate Student Association

Where: School of Electrical Engineering and Computer Science, Room SITE 5084, 800 King Edward Ave, Ottawa

When: Wednesday March 15th, 2017, 6:30 PM – 8:30 PM

Agenda: 6:30 – 6:45 PM Pizza / drinks and networking 6:45 – 8:15 PM Technical talk 8:15 – 8:30 PM Q&A / networking

Admission is free but registration is required via Eventbrite (https://www.eventbrite.ca/e/technical-talk-evolutionary-many-objective-optimization-tickets-32054295260)


Evolutionary many-objective optimization is a hot topic in the evolutionary computation community. A large number of new algorithms as well as a variety of modifications of existing algorithms have been proposed for the efficient handling of multi-objective problems with four or more objectives. In this presentation, first we check the increasing popularity of evolutionary many-objective optimization. Next, after briefly explaining the basic idea of evolutionary multi-objective optimization, we overview some well-known difficulties in many-objective optimization: search for Pareto optimal solutions, approximation of the entire Pareto front, presentation of a large number of non-dominated solutions, choice of a single final solution, and examination of the search behavior of evolutionary algorithms. Then we explain representative approaches to evolutionary many-objective optimization: modification of Pareto dominance, dimensionality reduction, incorporation of preference information, indicator-based algorithms, and decomposition-based algorithms. Our main focus is on the current trend in the field of evolutionary many-objective optimization, which is the proposal of new decomposition-based algorithms and their performance evaluation using DTLZ and WFG test problems. Finally, for discussing promising future research directions, we examine the following two issues: (i) choice of many-objective test problems and (ii) specifications in performance indicators. More specifically, we explain that the performance of recently proposed decomposition-based algorithms strongly depends on the choice of test problems as well as the specifications of reference points for the hypervolume (HV) and inverted generational distance (IGD) indicators. Examinations of these issues clearly show the importance of the following future research directions: proposal of a wide variety of many-objective test problems, examination of the reality of test problems (i.e., their relevance to real-world problems), development of a robust search mechanism with good performance over a wide variety of test problems with no adaptation, and development of an efficient adaptation mechanism of search strategies to a different test problem.

Speaker biography

Dr. Ishibuchi received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively. In 1992, he received the Ph. D. degree from Osaka Prefecture University where he has been a professor since 1999. He received a Best Paper Award from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011 and ACIIDS 2015. He also received a 2007 JSPS Prize (https://www.jsps.go.jp/english/e-jsps-prize/awards_4th_02.html). He was the IEEE CIS Vice-President for Technical Activities (2010-2013), the General Chair of ICMLA 2011, the Program Chair of IEEE CEC 2010 and IES 2014, and a Program/Technical Co-Chair of FUZZ-IEEE 2006, 2011-2013, 2015 and IEEE CEC 2013-2014, 2018. Currently, he is the Editor-in-Chief of IEEE CI Magazine (2014-2017), an IEEE CIS AdCom member (2014-2019), and an IEEE CIS Distinguished Lecturer (2015-2017). He is also an Associate Editor of IEEE TEVC (2007-2016), IEEE Access (2013-2016) and IEEE TCyb (2013-2016). He is an IEEE Fellow. His current research interests include fuzz classifier design, evolutionary multi-objective and many-objective optimization, and evolutionary games. According to Google Scholar, the total number of citations of his publications is about 20,000 and his h-index is 62. According to the Most Cited Researchers List developed for ShanghaiRanking’s Global Ranking of Academic Subjects 2016 by Elsevier (http://www.shanghairanking.com/The-Most-Cited-Researchers-Developed-for-ShanghaiRanking-Global-Ranking-of-Academic-Subjects-2016-by-Elsevier.html), he is included in the most cited 300 researchers in the field of Computer Science and Engineering.

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