Mendel Conference
25th International Conference on Soft Computing, July 10-12 Brno, Czech Republic
2019 Invited Speakers and Tutorials

High-Performance Evolutionary Algorithms

Professor Kenneth A. De Jong
Department of Computer Science
George Mason University
United States

The success of the use of evolutionary algorithms to solve difficult computational problems has led to its continued application to problems of increasing size and complexity. The result is that standard “off-the-shelf” EAs often generate less than adequate performance both in the time required to find acceptable solutions and/or the quality of the final results. If one adopts a “no free lunch” perspective, successful applications will require the EA practitioner to match EA algorithm properties with the properties of the applications.

In this talk I will describe such a matching framework and illustrate its use in designing high-performance EAs for a number of difficult computational problems.

Kenneth A. De Jong is a senior and well-known researcher in the EC community with a rich and diverse research profile. De Jong's research interests include genetic algorithms, evolutionary computation, machine learning, and adaptive systems. He is currently involved in research projects involving the development of new evolutionary algorithm (EA) theory, the use of EAs as heuristics for NP-hard problems, and the application of EAs to the problem of learning task programs in domains such as robotics, diagnostics, navigation and game playing. Support for these projects is provided by NSF, DARPA, ONR, and NRL. He is an active member of the Evolutionary Computation research community and has been involved in organizing many of the workshops and conferences in this area. He is the founding editor-in-chief of the journal Evolutionary Computation (MIT Press), and a member of the board of ACM SIGEVO.

More information can be found here.

Where is the research on
evolutionary multi-objective optimization heading to?

Dr. Carlos A. Coello Coello
Mexico City

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 33 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 33 years of existence, is just beginning to mature, will be briefly mentioned.

Carlos Artemio Coello received a MSc and a PhD in Computer Science in 1993 and 1996, respectively. 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 at CINVESTAV-IPN in Mexico City, Mexico. He has published over 390 papers in international peer-reviewed journals, book chapters, and conferences. He has also co-authored the book "Evolutionary Algorithms for Solving Multi-Objective Problems", which is now in its Second Edition (Springer, 2007) and has co-edited the book "Applications of Multi-Objective Evolutionary Algorithms" (World Scientific, 2004). His publications currently report over 23,600 citations, according to Google Scholar (his h-index is 61). He has delivered invited talks, keynote speeches and tutorials at international conferences held in Spain, USA, Canada, Switzerland, UK, Chile, Colombia, Brazil, Argentina, China, India, Uruguay and Mexico.
Dr. Coello has pioneered an area now known as multi-objective evolutionary optimization, which focuses on solving optimization problems with two or more objective functions (usually in conflict with each other) using biologically inspired algorithms. Dr. Coello has more than 470 publications (including 1 monographic book in English, more than 140 articles in peer-reviewed journals and 55 chapters in books), which currently report more than 44,700 citations in Google Scholar (his h index is 83). He is also an Associate Editor of several international journals, including the two most important in his area (IEEE Transactions on Evolutionary Computation and Evolutionary Computation). He is also a member of the Advisory Board of Springer's Natural Computing Book Series.

More information can be found here.

Evacuation dynamics in the course of terrorist attacks

Prof. René Lozi
CNRS, Math laboratory
Université Côte d'Azur

We focus on a new model of human behaviour in the context of urban terrorist attack. One of the major challenges during such event is to assure the protection of the population. In this goal, it is important to better understand and anticipate both individual and collective human behaviour, and dynamics of displacements of the crowd associated with these behaviours.
The model we propose is based on the Panic-Control-Reflex model recently published, which is improved in order to take into account the role of spatial configurations on behavioural dynamics. It incorporates via a bottleneck effect the narrowness and the length of the streets, the pressure and counter pressure of the crowd in dangerous and safe places. The numerical simulations highlight that, depending on their respective size, intermediate places will modulate the dynamics and the speed of flow of the crowd. This model used with a friendly graphical representation, allows planners to think accurately where to organize host festive events in specific territorial context.

Professor René Lozi received his Ph.D. (on bifurcation theory) from the University of Nice in 1975 and the French State Thesis under the supervision of Prof. René Thom in 1983. In 1991, he became Full Professor at Laboratoire J. A. Dieudonné, University of Nice and IUFM (Institut Universitaire de Formation des Maîtres). He has served as the Director of IUFM (2001-2006). He is member of the Editorial Board of Indian J. of Industrial and Appl. Maths and J. of Nonlinear Systems and Appl., and member of the Honorary Editorial Board of Intern. J. of Bifurcation & Chaos. In 1977 he entered the domain of dynamical systems, in which he discovered a particular mapping of the plane producing a very simple strange attractor (now known as the "Lozi map"). Nowadays his research areas include complexity and emergences theories, dynamical systems, bifurcation and chaos, control of chaos and cryptography based chaos, and recently memristor. He is working in this field with renowned researchers, such as Professors Leon O. Chua (inventor of "Chua circuit" and Memristor) and Alexander Sharkovsky (who introduced the "Sharkovsky's order"). He received the Dr. Zakir Husain Award 2012 of the Indian Society of Industrial and Applied Mathematics during the 12th biannual conference of ISIAM at the university of Punjab, Patialia, January 2015.

More information can be found here.

Learning to Optimize and Optimal Learning

Prof. Juergen Branke
Warwick Business School
The University of Warwick
United Kingdom

This talk discusses the relationship between machine learning and optimisation. It demonstrates that many machine learning problems are actually optimisation problems, and could benefit from advances in operational research. On the other hand, the latest challenges in optimisation, such as parameter tuning, algorithm selection, Hyper heuristics or handling of uncertainty are actually closely related to machine learning. Furthermore, recent algorithmic developments such as Bayesian Optimisation very much blur the boundary between machine learning and optimisation, as they explicitly combine learning about the search space with optimisation.

Juergen Branke is Professor of Operational Research and Systems at Warwick Business School. He has been an active researcher in the area of nature-inspired optimisation for almost 25 years, and has published over 170 papers in international peer-reviewed journals and conferences that have been cited over 11,000 times according to Google Scholar. His main research interests include the handling of uncertainty, dynamically changing optimisation problems and multiobjective optimisation. Prof. Branke is Area Editor for the Journal of Heuristics, and Associate Editor for IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal, and the Journal of Multi-Criteria Decision Analysis. He was also programme (co-)chair of various important conferences in the area, including Genetic and Evolutionary Computation Conference (2010) and Parallel Problem Solving from Nature (2014).

More information can be found here.

How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation

Pietro S. Oliveto, Ph.D.
Vice-Chancellor Fellow
University of Sheffield
United Kingdom

Rigorous computational complexity analyses of evolutionary algorithms have been performed since the nineties. The first results were inevitably related to simplified algorithms for benchmark problems with significant structures. Nowadays analyses are possible for classical combinatorial optimisation problems with real world applications and for the standard bio-inspired optimisation algorithms that are used in practice. Such analyses shed light on which classes of algorithms should be preferred for different problems and on how to set the algorithmic parameters. In this talk I will provide examples of the insights which can be gained from these theoretical analyses and how they can ultimately lead to better algorithms, parameter settings and results in practice.

Pietro S. Oliveto is a Senior Lecturer and EPSRC Early Career Fellow at the Department of Computer Science, University of Sheffield where he leads the ’Rigorous Runtime Analysis of Bio-inspired Computing’ project team, ‘Rigorous Research’ in short. He received the Laurea degree in computer science from the University of Catania, Italy in 2005 and the PhD degree in computational complexity of evolutionary algorithms from the University of Birmingham, UK in 2009. He has been an EPSRC PhD Plus Fellow (2009-2010) and an EPSRC Postdoctoral Fellow in Theoretical Computer Science (2010-2013) at Birmingham and a Vice-Chancellor’s Fellow (2013-2016) at Sheffield.
Dr. Oliveto is Chair of the IEEE Computational Intelligence Society (CIS) Technical Committee for Evolutionary Computation (ECTC). He is Associate Editor of IEEE Transactions on Evolutionary Computation (IEEE TEVC) and Editorial Board member of the Algorithms Journal. He has edited special issues of the IEEE TEVC, Evolutionary Computation (ECJ) and Theoretical Computer Science (TCS) journals. He has Chaired the annual Symposium on Foundations of Computational Intelligence (IEEE FOCI) (2014-2020) and is a Steering Committee member of the workshop series on Theory of Randomised Search Heuristics (ThRaSH).

More information can be found here.

Real-Parameter Single Objective Optimization with Differential Evolution: Algorithms jSO and jDE100

prof. Janez Brest
Laboratory for Computer Architecture and Languages
Institute of Computer Science
University of Maribor

Differential Evolution (DE) algorithm is a simple, yet powerful optimization algorithm. It is widely used in real-parameter optimization, as well as in discrete optimization in many domains, such as single objective optimization, constrained optimization, multi-modal optimization, and multi-objective optimization. Storn and Price proposed the DE algorithm more than 20 years ago. The original DE algorithm has been modified in many directions, such as new mutation operators, adaptive and/or self-adaptive control parameters, ensembles, combined with local search heuristics, and many others. This talk will present two recent developments of the DE algorithm for single objective real-parameter optimization, algorithms jSO and jDE100. The first one was efficient on solving CEC 2017 benchmark functions; the second one has been used for participating at 100-Digit Challenge (CEC 2019, GECCO 2019, SEMCCO 2019)

J. Brest received his Ph.D. degree in computer science from the University of Maribor, Maribor, Slovenia, in 2000. He has been with the Laboratory for Computer Architecture and Languages, at the Faculty of Electrical Engineering and Computer Science of the University of Maribor, since 1993. He is currently a Full Professor and Head of the Laboratory for Computer Architecture and Programming Languages at the Institute of Computer Science, at the University of Maribor, Slovenia.
His research interests include evolutionary computing, artificial intelligence, optimization, and parallel and distributed computing.

More information can be found here.

 Commercial Tutorial
Optimization and Global Optimization using MATLAB

Jan Studnicka, MSc.
Senior Application Engineer
International Reseller of MathWorks, Inc., U.S.A.
Czech Republic

A brief overview of MATLAB tools for different types of optimization. We will introduce the possibilities of solving a wide range of optimization tasks using Optimization and Global Optimization Toolbox, Bayesian optimization of hyperparameters in machine learning algorithms, optimization of parameters in Simulink models, portfolio optimization, acceleration with parallel computing and deployment of MATLAB optimization models.

More information can be found here.

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