Prof. Panos M. Pardalos

Distinguished Professor, Department of Industrial and Systems Engineering, University of Florida, USA

Biography: Dr. Panos Pardalos is a Distinguished Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science & Information & Engineering departments. In addition, he is the director of the Center for Applied Optimization. Dr. Pardalos is a world renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, and Data Sciences. He is a Fellow of AAAS, AIMBE, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Dr. Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for "scientific contributions that stand the test of time." Dr. Pardalos has been awarded a prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher's entire achievements to date - fundamental discoveries, new theories, insights that have had significant impact on their discipline. Dr. Pardalos is also a Member of several Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, Computational Management Science, and Springer Nature Operations Research Forum. He has published over 500 journal papers, and edited/authored over 200 books. He is one of the most cited authors and has graduated 64 PhD students so far. Details can be found in


Prof. Carlos A. Coello Coello

Fellow IEEE, Editor-in-Chief, IEEE Transactions on Evolutionary Computation, Department of Computer Science CINVESTAV-IPN, Mexico

Biography: Carlos Artemio Coello Coello received a PhD in Computer Science from Tulane University (USA) in 1996. He currently has over 500 publications which, according to Google Scholar, report over 54,800 citations (with an h-index of 94). He has received several awards, including the National Research Award (in 2007) from the Mexican Academy of Science (in the area of exact sciences), 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). the prestigious 2013 IEEE Kiyo Tomiyasu Award, "for pioneering contributions to single- and multiobjective optimization techniques using bioinspired metaheuristics", of the 2016 The World Academy of Sciences (TWAS) Award in "Engineering Sciences" and of the 2021 IEEE CIS Evolutionary Computation Pioneer Award. Since January 2011, he is an IEEE Fellow. Since 2010, he is a Full Professor with distinction at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico. He specializes on the design of metaheuristics for solving nonlinear multi-objective problems. He is currently the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation.

Speech Title: Where is the Research on Evolutionary Multi-objective Optimization Heading to?

Abstract: The first multi-objective evolutionary algorithm was published in 1985. However, it was not until the late 1990s that so-called evolutionary multi-objective optimization began to gain popularity as a research area. Throughout these 36 years, there have been several important advances in the area, including the development of different families of algorithms, test problems, performance indicators, hybrid methods and real-world applications, among many others. In the first part of this talk we will take a quick look at some of these developments, focusing mainly on some of the most important recent achievements. In the second part of the talk, a critical analysis will be made of the by analogy research that has proliferated in recent years in specialized journals and conferences (perhaps as a side effect of the abundance of publications in this area). Much of this research has a very low level of innovation and almost no scientific input, but is backed by a large number of statistical tables and analyses. In the third and final part of the talk, some of the future research challenges for this area, which, after 36 years of existence, is just beginning to mature, will be briefly mentioned.


Prof. Ke Tang

Department of Computer Science and Engineering, Southern University of Science and Technology, China

Biography: Ke Tang is a Professor at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech). Before joining SUSTech in January 2018, he was with the School of Computer Science and Technology, University of Science and Technology of China (USTC), first as an Associate Professor (2007-2011) and then as a Professor (2011-2017). His major research interests include evolutionary computation and machine learning, particularly in large-scale evolutionary computation, integration of evolutionary computation and machine learning, as well as their applications.He has published more than 180 papers, which have received over 10000 Google Scholar citations with an H-index of 48. Professor Tang is a recipient of the IEEE Computational Intelligence Society Outstanding Early Career Award (2018), the Newton Advanced Fellowship (Royal Society, 2015) and the Natural Science Award of Ministry of Education of China (2011 and 2017). He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and served as a member of Editorial Boards for a few other journals .

Speech Title: Co-Evolved Parallel Algorithm Portfolios

Abstract: Parallel Algorithm Portfolios (PAPs), being generally applicable to nearly all kinds of computation (optimization/decision/counting/learning) problems and friendly to modern parallel computing facilities, has become a framework adopted by many industrial software systems. On the other hand, to configure a good PAP in practice has emerged as a tedious and challenging problem. For it population-based search nature, Evolutionary Computation, in particular, co-evolution, offers some off-the-shelf ideas for automated PAP configuration, which will be introduced in this talk. Specifically, we will show that, when the training instances are sufficient, high-performance PAPs can be automatically constructed with little human effort involved, by a co-evolutionary approach. In case the sample is of small size or biased, which is often encountered in practice, we propose to use competitive co-evolution of the PAPs and the instance set to tackle such challenges. The codes and datasets are available at