Prof. Carlos Coello Coello
Title : Recent Results and Open Problems in Evolutionary Multiobjective Optimization
Abstract : Evolutionary algorithms (as well as a number of other metaheuristics) have become a popular choice for solving problems having two or more (often conflicting) objectives (the so-called multi-objective optimization problems).This area, known as EMOO (Evolutionary Multi-Objective Optimization) has had an important growth in the last 20 years, and several people (particularly newcomers) get the impression that it is now very difficult to make contributions of sufficient value to justify, for example, a PhD thesis. However, a lot of interesting research is still under way. In this talk, we will review some of the research topics on evolutionary multi-objective optimization that are currently attracting a lot of interest (e.g., handling many objectives, hybridization,indicator-based selection, use of surrogates, etc.) and which represent good opportunities for doing research.Some of the challenges currently faced by this disciplinewill also be delineated.
Short Bio: Carlos Artemio Coello Coello received a PhD in Computer Science from Tulane University (in theUSA) in 1996. His PhD thesis was one of the first in a field which is now called “evolutionary multiobjective optimization”.
He currently has over 450 publications which report over 34,000 citations in Google Scholar (his h-index is 74).
Dr. Coello has been a Senior Research Fellow in the Plymouth Engineering Design Centre (in England) and a Visiting Professor at DePauw University (in the USA).He is currently full professor with distinction (Investigador Cinvestav 3F) at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico.
He currently serves as associate editor of several journals, including “IEEE Transactions on Evolutionary Computation”, “Evolutionary Computation”, “Journal of Heuristics”, “Pattern Analysis and Applications”, “Applied Soft Computing” and “Computational Optimization and Applications”.
He has received several national awards, including the “National Research Award” from the Mexican Academy of Science (in 2007), the “Medal to the Scientific Merit” from Mexico City’s Congress, the “Ciudad Capital:Heberto Castillo 2011 Award” in Basic Science,the “2012 Scopus Award” (Mexico’s edition), and the “2012 National Medal of Science in Physics, Mathematics and Natural Sciences” from Mexico’s Presidency (this is the most important award that a scientist can receive in Mexico). He has also received several international awards, including the prestigious “2013
IEEE Kiyo Tomiyasu Award” and the “2016 Third World Academy of Sciences (TWAS) Award in Engineering Sciences”.Since January 2011, he is an IEEE Fellow for his “contributions to multi-objective optimization and constraint-handling techniques”.
Prof. Jie Lu
Title : Fuzzy Transfer Learning for Prediction and Decision Making
Abstract : This presentation highlights the value of fuzzy transfer learning methods and related algorithms for handling complex prediction problems in rapidly-changing data distribution and data-shortage situations. It provides a framework for utilizing previously-acquired knowledge to predict new but similar problems quickly and effectively by using fuzzy set techniques. It systematically presents developments in fuzzy set-based transfer learning methods for prediction, including fuzzy transfer learning-based prediction framework, fuzzy domain adaptation, fuzzy cross-domain adaptation, and in particular, cross-domain adaptive fuzzy inference system, and their respective applications in prediction and decision support. This presentation demonstrates the successful use of fuzzy techniques in facilitating the incorporation of approximation and expressiveness of data uncertainties within knowledge transfer, machine learning and data-driven decision support systems.
Short Bio: Distinguished Professor Jie Lu is an internationally established scientist in the areas of decision support systems, fuzzy transfer learning, concept drift, recommender systems, prediction and early warning systems. She is the Associate Dean in Research Excellence in the Faculty of Engineering and Information Technology at the University of Technology Sydney. She is also the Director of the Centre for Artificial Intelligence (CAI). She has published six research books and more than 400 papers in refereed journals and conference proceedings. She has won eight Australian Research Council (ARC) discovery grants and 10 other research grants in the last 15 years. She serves as Editor-In-Chief for Knowledge-Based Systems (Elsevier) and as Editor-In-Chief for International Journal on Computational Intelligence Systems (Atlantis), has delivered 15 keynote speeches at international conferences, and has chaired 10 international conferences. She is an ARC panel member (2016-2018).