Dr. Bobby R. Bruce, Ph.D.
Software Systems Engineering Group
University College London
United Kingdom

Genetic improvement is an emerging area of software engineering where code is automatically modified by search-based processes. In the last few years, researchers in this field have produced tools and frameworks shown to be human competitive and suitable for solving real-world software engineering problems. This talk shall give an introduction to genetic improvement, discuss the state-of-the-art, and explain how genetic improvement tools are built. Bobby will conclude the talk with a short tutorial on optimising a piece of software’s execution time using the ‘Genetic Improvement in No time’ framework (GIN — available at https://github.com/gintool/gin ).

Bobby R. Bruce is a software engineering researcher specialising in the emerging area of genetic improvement -- the use of search, particularly evolutionary search, to improve software with respect to some user-defined objective. His PhD thesis, ‘The Blind Software Engineer’ focused on the the use of such techniques to improve software’s non-functional properties. Having completed his PhD in software engineering at UCL's Centre for Research on Evolutionary Search and Testing (CREST), Bobby is starting a position as a post-doctorial scholar at the University of California, Los Angeles, focusing on the dynamic optimisation of Java bytecode. He remains interested in genetic improvement, particularly in how it may be used to aid in the parallelisation of software; a task which human developers typically find difficult.

More information can be found here.

Dr. Aleš Zamuda, Ph.D.
Institute of Computer Science | Computer Architecture Lab.
University of Maribor
Slovenia

Differential Evolution (DE) as a flagship algorithm for evolutionary computation, is applicable to vast amount of challenges. Three example approaches using DE with different requirements will be presented, emphasizing the perspective of large scale optimization, time constraints, and big data assisted optimization model tailoring. In this perspective, the three examples are given from computer vision, energy, and robotics. For computer vision, spatial procedural tree growth and sway animation reconstruction from processing multiple view images with DE will be presented. In energy domain, a top performing system for hydrothermal scheduling using DE will be highlighted, which uses a tailored model that is assisted with big data approaches for surrogate optimization. In robotics and navigation, underwater glider path planning challenges will be presented, where evolutionary computation has been used for first time and significantly improves vessel capabilities by improving navigation robustness and optimizing trajectories.

Aleš Zamuda is an Assistant Professor and Researcher at University of Maribor (UM), Slovenia. He received Ph.D. (2012), M.Sc. (2008), and B.Sc. (2006) degrees in computer science from UM. He is management committee (MC) member for Slovenia at European Cooperation in Science (COST), actions CA15140 (ImAppNIO – Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice) and IC1406 (cHiPSet – High-Performance Modelling and Simulation for Big Data Applications). He is vice-chair at IEEE Slovenia Section, IEEE Senior Member, IEEE Young Professionals chair for IEEE Slovenia Section, IEEE CIS member, ACM SIGEVO member, ImAppNIO Benchmarks working group vice-chair, and editorial board member (associate editor) for Swarm and Evolutionary Computation (2017 IF=3.893). His areas of computer science applications include ecosystems, evolutionary algorithms, multicriterion optimization, artificial life, and computer animation; currently yielding h- index 16, 38 publications, and 742 citations on Scopus. He won IEEE Region 8 SPC 2007 award, IEEE CEC 2009 ECiDUE, 2016 Danubuius Young Scientist Award, several excellence awards from UM and is a 1% top reviewer at 2017 Publons Peer Review Awards, including reviews for 40 journals and 65 conferences.

More information can be found here.

Dr. Michal Bidlo, Ph.D.
Department of Computer Systems
Brno University of Technology
Czech Republic

In this talk the basic principles and applications of cellular automata (CA) will be introduced and some recent results regarding evolutionary design of complex CA will be presented. In particular, the talk will be devoted to multi-state uniform cellular automata. As the recent research has shown, the representation of transition functions of CA for evolutionary algorithms may significantly improve the results that can be obtained for various case studies. Specifically, the generic square calculation problem in 1D CA and pattern development problem in 2D CA will be considered. It will be demonstrated that various square calculations in cellular automata can be discovered, some of which substantially overcome the known solution. Moreover, several results of the pattern development will be shown which have not been achieved before using conventional design methods. Some of the results indicate that an exact CA behaviour can be automatically designed by evolution even for cellular automata working with more than 10 cell states.

Dr. Michal Bidlo is an assistant professor at Brno University of Technology (BUT), Faculty of Information technology (FIT), Czech Republic. Since the beginning of his career dr. Bidlo has participated in the research of Evolvable Hardware group at BUT FIT with the main research interests including natural computation, cellular automata, evolutionary deisgn, evolvable hardware and bio-inspired systems. He is the author or co-author of over 20 conference/journal papers in these areas. He has published at renowned international conferences and in international journals (e.g. IEEE Transactions on Evolutionary Computation or Soft Computing) and has served as an active PC member of several international conferences (e.g. DATE, GECCO, CEC, ICES). In addition to his research, dr. Bidlo teaches several courses at BUT FIT, including Applied Evolutionary Algorithms, Bio-Inspired Computers or Design of Computer Systems. For his research work he was awarded by Silver medal at the international competition HUMIES - Awards for Human-Competitive Results Produced by Genetic and Evolutionary Computation in 2016 for his method for the evolutionary design of complex cellular automata, and received the Best Paper Award at 2016 International Joint Conference on Computational Intelligence - Evolutionary Computation Theory and Applications.

More information can be found here.

assoc. Prof. Roman Senkerik, Ph.D.
Department of Informatics and Artificial Intelligence
Tomas Bata University in Zlin
Czech Republic

This tutorial represents an insight into an attractive open research task, which is a novel method for visualizing the dynamics of evolutionary and swarm-based algorithms in the form of networks. The idea is based on the similarity in interactions between individuals in the metaheuristics algorithms and for example, users of social networks, linking between web pages, etc. The population is visualized as an evolving complex network that exhibits non-trivial features. The features like clustering, centralities, communities, and many more, offer a clear description of the population under evaluation. This tutorial shows the differences between the types of complex networks used, variations in building complex networks to capture the population dynamics of evolutionary algorithms or communication inside swarm-based algorithms, investigation on the time development of the network. It also shows several successful utilization of complex networks attributes for the performance improvements through the adaptive population as well as parameter/strategy control, further the possibility of controlling the evolution through complex network features.

Roman Senkerik was born in the Czech Republic, and went to the Tomas Bata University in Zlin, Faculty of applied informatics, where he studied Technical Cybernetics and obtained his MSc degree in 2004, Ph.D. degree in 2008 (Technical Cybernetics) and Assoc. prof. degree in 2013 (VSB – Technical University of Ostrava – degree in Informatics). He is now a researcher and lecturer at the same university. He was IPC member or session chair of many important international conferences IEEE CEC, IEEE SSCI, ECMS, MENDEL, HAIS etc. He is Recognized Reviewer for many Elsevier journals as well as many other leading journals in computer science/computational intelligence. He was a part of the organizing teams for Special Sessions/symposiums at IEEE CEC and IEEE SSCI events. He was a guest editor of several Special Issues in journals, editor of Springer proceedings for several conferences.

More information can be found here.

Jaroslav Jirkovsky, Ph.D.
Senior Application Engineer
HUMUSOFT s.r.o.
International Reseller of MathWorks, Inc., U.S.A.
Czech Republic

Convolutional neural networks are essential tools for deep learning, and are especially useful for image classification, object detection, and recognition tasks. CNNs are implemented as a series of interconnected layers. In MATLAB, you can construct a CNN architecture, train a network, and use the trained network to predict class labels or detect objects using R-CNN, Fast R-CNN and Faster R-CNN object detectors. You can also extract features from a pretrained network, and use these features to train a classifier, or train convolutional neural networks for regression tasks. What the network learns during training is sometimes unclear. Deep Dream is a feature visualization technique in deep learning that synthesizes images that strongly activate network layers. By visualizing these images, you can highlight the image features learned by a network.

More information can be found here.

Prof. dr. Thomas Bäck
Professor for Natural Computing
Head of the Natural Computing Research Group
Leiden Inst of Advanced Computer Science (LIACS)
Leiden University
Netherlands

Industrial optimization problems often characterized by a number of challenging properties, such as time-consuming function evaluations, high dimensionality, a large number of constraints, and multiple optimization criteria. Working with Evolutionary Strategies, we have optimized them over the past decades for such optimization problems. In this presentation, we will illustrate these aspects by referring to industrial optimization problems, such as they occur in the automotive and many other industries. We will show that evolutionary strategies can be very effective even in case of very small numbers of functions evaluations. In the second part of the talk, some recent experiments on configuring evolutionary strategies are presented, showing that evolutionary strategies can be further improved by automatic search methods. This opens up a promising new direction towards constructing optimization algorithms based on a modularized evolutionary strategy superstructure. Moreover, combining this with data mining techniques, it is possible to characterize the relation between problem characteristics and beneficial algorithmic features.

Thomas Bäck is full professor of computer science at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands, where he is head of the Natural Computing group since 2002. He received his PhD (adviser: Hans-Paul Schwefel) in computer science from Dortmund University, Germany, in 1994. From 2000 - 2009, Thomas was Managing Director of NuTech Solutions GmbH and CTO of NuTech Solutions, Inc. He gained ample experience in solving real-life problems in optimization and data mining through working with global enterprises such as BMW, Beiersdorf, Daimler, Ford, Honda, and many others. Thomas Bäck has more than 200 publications on natural computing, as well as two books on evolutionary algorithms: Evolutionary Algorithms in Theory and Practice (1996), Contemporary Evolution Strategies (2013). He is co-editor of the Handbook of Evolutionary Computation, and most recently, the Handbook of Natural Computing. He is also editorial board member and associate editor of a number of journals on evolutionary and natural computing. Thomas received the best dissertation award from the German Society of Computer Science (Gesellschaft für Informatik, GI) in 1995 and is an elected fellow of the International Society for Genetic and Evolutionary Computation for his contributions to the field.

More information can be found here.

Dr Marco Castellani, Ph.D
Department of Mechanical Engineering
University of Birmingham
United Kingdom

Darwin recognised predator-prey mechanisms as a major driver of natural evolution. This talk discusses the competitive co-evolutionary training of multi-layer perceptron (MLP) neural networks. Classical evolutionary algorithms evolve a population of MLPs, measuring the fitness of the individuals from their capability to correctly map a fixed set of training examples. Competitive co-evolutionary algorithms pit a population of MLPs to a population of training patterns. The classifiers are regarded as predators that need to ‘capture’ (correctly categorise or map) the prey (training patterns). Success for the predators is measured on their ability to capture the prey. Success for the prey is measured on their ability to escape predation (be mapped incorrectly). The aim of the procedure is to create an evolutionary tug-of-war between the best classifiers and the most difficult data samples. Tested on different classification tasks, competitive co-evolution showed promise in terms of robustness to corrupted data patterns, accuracy of the solutions, and reduced computational costs. Thanks to its ability to focus yet unlearned examples, competitive co-evolution showed also great promise on tasks involving unbalanced data classes.

Marco Castellani is Lecturer in Advanced Robotics and Intelligent Systems at the Department of Mechanical Engineering of the University of Birmingham. He has 20 years of research experience in the private sector, universities, and research centres in various European countries. His work spanned a broad interdisciplinary area encompassing engineering, biology, and computer science, including machine learning, machine vision, pattern recognition, swarm intelligence, soft computing, intelligent control, optimisation, ecological modelling, natural language processing, and the general AI field. He published about 50 peer-reviewed research papers in scientific journals and international conferences, and is currently Editor of the Cogent Engineering journal.

More information can be found here.

Dr. Swagatam Das, Ph.D.
Electronics and Communication Sciences Unit
Indian Statistical Institute, Kolkata
India

Data emerging from the real world may very often be plagued with prominent irregularities including class imbalance (under represented classes in the training sets), missing or absent features, and small disjuncts (under-represented sub-concepts within classes). Performances of the traditional classifiers are usually far from their theoretical limits in face of such data irregularities. This talk will outline some very effective recent approaches based on Support Vector Machines (SVMs), boosting, and k-Nearest Neighbor Classifiers (kNNs) to handle such irregularities. The talk will also discuss a few open-ended research issues in this direction.

Swagatam Das is currently serving as a faculty member at the Electronics and Communication Sciences Unit of the Indian Statistical Institute, Kolkata, India. His research interests include machine learning and non-convex optimization. Dr. Das has published more than 250 research articles in peer-reviewed journals and international conferences. He is the founding co-editor-in-chief of Swarm and Evolutionary Computation, an international journal from Elsevier. He has also served as or is serving as the associate editors of the IEEE Trans. on Systems, Man, and Cybernetics: Systems, IEEE Computational Intelligence Magazine, IEEE Access, Pattern Recognition (Elsevier), Neurocomputing (Elsevier), Engineering Applications of Artificial Intelligence (Elsevier), and Information Sciences (Elsevier). Dr. Das has 11500+ Google Scholar citations and an H-index of 53 till date. He has acted as guest editors for special issues in journals like IEEE Transactions on Evolutionary Computation, ACM Transactions on Adaptive and Autonomous Systems and IEEE Transactions on SMC, Part C. He is the recipient of the 2012 Young Engineer Award from the Indian National Academy of Engineering (INAE). He is also the recipient of the 2015 Thomson Reuters Research Excellence India Citation Award as the highest cited researcher from India in Engineering and Computer Science category between 2010 to 2014.

More information can be found here.

Dr. Carola Doerr, Ph.D.
French National Center for Scientific Research
Université Pierre et Marie Curie
France

Evolutionary algorithms (EAs) are bio-inspired heuristics, that–thanks to their high flexibility and their ability to produce high-quality solutions for a broad range of problems–are today well-established problem solvers in industrial and academic applications. EAs are often used as subroutines for particularly difficult parts of the optimization problem, as well as for several pre- or post-processing steps of state of the art optimization routines. The predominant part of research on EAs focuses on engineering and empirical work. This research is complemented by the theory of evolutionary computation, which aims at providing mathematically founded statements about the working principles of such optimization techniques. Historically, the role of theory of evolutionary computation was somewhat restricted to debunking common misconceptions about the performance of EAs. More recently, we see an emerging number of examples where theory has served a source of inspiration to design more efficient EAs. In this talk, we shall discuss some of these recent developments. Our main focus will be
(1) on the role of crossover in evolutionary computation, and
(2) on the importance of dynamic parameter choices.

Carola Doerr is a CNRS researcher at the Université Pierre et Marie Curie (Paris 6). She studied mathematics at Kiel University (Germany, Diploma in 2007) and computer science at the Max Planck Institute for Informatics and Saarland University (Germany, PhD in 2011). From Dec. 2007 to Nov. 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, mainly in the area of network optimization, where she has used randomized search heuristics to compute more efficient network layouts and schedules. Before joining the CNRS she was a post-doc at the Université Diderot (Paris 7) and the Max Planck Institute for Informatics. Carola Doerr's research interest is in the theory of randomized algorithms---both in the design of efficient algorithms as well as in finding a suitable complexity theory for randomized search heuristics. After contributing to the revival of black-box complexity, a theory-guided approach to explore the limitations of heuristic search algorithms, she recently started a series of works aimed at exploiting insights from the theory of evolutionary computation to design more efficient EAs, in particular such with a dynamic choice of parameters.

More information can be found here.

Prof. Shengxiang Yang
Centre for Computational Intelligence (CCI)
School of Computer Science and Informatics
De Montfort University
United Kingdom

Evolutionary Computation (EC) encapsulates a class of stochastic optimisation algorithms, which are inspired by principles from natural and biological evolution. EC has been widely used for optimisation problems in many fields. Traditionally, EC methods have been applied for solving static problems. However, many real world problems are dynamic optimisation problems (DOPs), which are subject to changes over time due to many factors. DOPs have attracted a growing interest from the EC community in recent years due to the importance in the real-world applications of EC. This talk will first briefly introduce the concept of EC and DOPs, then review the main approaches developed to enhance EC methods for solving DOPs, and describe several detailed approaches developed for EC methods for DOPs. Finally, some conclusions will be made based on the work presented and the future work on EC for DOPs will be briefly discussed.

Shengxiang Yang is now a Professor of Computational Intelligence (CI) and the Director of the Centre for Computational Intelligence, De Montfort University, UK. He has worked extensively for 20 years in the areas of CI methods, including EC and artificial neural networks, and their applications for real-world problems. He has over 230 publications in these domains. He has 5800+ Google Scholar citations and an H-index of 40. His work has been supported by UK research councils (e.g., Engineering and Physical Sciences Research Council (EPSRC), Royal Society, and Royal Academy of Engineering), EU FP7 and Horizon 2020, Chinese Ministry of Education, and industry partners (e.g., BT, Honda, Rail Safety and Standards Board, and Network Rail, etc.), with a total funding of over Ł2M, of which two EPSRC standard research projects have been focused on EC for DOPs. He serves as an Associate Editor or Editorial Board Member of eight international journals, including IEEE Transactions on Cybernetics, Evolutionary Computation, Neurocomuting, Information Sciences, and Soft Computing. He is the founding chair of the Task Force on Intelligent Network Systems (TF-INS) and the chair of the Task Force on EC in Dynamic and Uncertain Environments (ECiDUEs) of the IEEE CI Society (CIS). He has organised/chaired over 30 workshops and special sessions relevant to ECiDUEs for several major international conferences. He is the founding co-chair of the IEEE Symposium on CI in Dynamic and Uncertain Environments. He has co-edited 12 books, proceedings, and journal special issues. He has been invited to give over 10 keynote speeches/tutorials at international conferences, and over 30 seminars in different countries.

More information can be found here.

Prof. Krzysztof Krawiec
Institute of Computing Science
Poznan University of Technology
Poland

In genetic programming and other types of test-based problems, candidate solutions interact with multiple tests in order to be evaluated. The conventional approach involves aggregating the interaction outcomes into a scalar objective. However, passing different tests may require unrelated `skills' that candidate solutions may vary on. Scalar fitness is inherently incapable of capturing such differences and leaves a search algorithm largely uninformed about the diverse qualities of individual candidate solutions. In this talk, I will discuss the implications of this fact and present a range of new methods that avoid scalarization by turning the outcomes of interactions between programs and tests into 'search drivers' -- heuristic, transient pseudo-objectives that form multifaceted characterizations of candidate solutions. I will also demonstrate the feasibility of this approach with experimental evidence and embed this research into a broader context of behavioral program synthesis.  

Krzysztof Krawiec is an Associate Professor in the Institute of Computing Science at Poznan University of Technology, Poland, where he heads the Computational Intelligence Group. His primary research areas are genetic programming, machine learning, and coevolutionary algorithms, with applications in program synthesis, modeling, pattern recognition, and games. Dr. Krawiec co-chaired the European Conference on Genetic Programming in 2013 and 2014, GP track at GECCO'16, is an associate editor of Genetic Programming and Evolvable Machines journal, and has been a visiting researcher at Computer Science and Artificial Intelligence Laboratory at MIT and Centre for Research in Intelligent Systems at University of California.

More information can be found here.

Dr. Jiri Sima, DrSc.
Department of Theoretical Computer Science
Institute of Computer Science
The Czech Academy of Sciences
Czech Republic

(Artificial) neural networks (NN) are biologically inspired computational devices that are alternative to conventional computers, especially in the area of machine learning, with a plethora of successful commercial applications in AI. The limits and potential of particular NNs for general-purpose computation have been studied by classifying them within the Chomsky hierarchy (e.g. finite or pushdown automata, Turing machines) and/or more refined complexity classes (e.g. polynomial time). It has been shown that the computational power of NNs basically depends on the information contents of weight parameters. For example, the analysis is fined-grained when changing from rational to arbitrary real weights while the classification is still not complete between integer and rational weights. For this purpose, we introduce an intermediate model of integer-weight NNs with one extra analog neuron having rational weights and we classify this model within the Chomsky hierarchy roughly between context-free and context sensitive languages. Our analysis reveals an interesting link to an active research field on non-standard positional numeral systems with non-integer bases.
In our talk we will briefly survey the basic concepts and results concerning the computational power of neural networks. Then we will discuss the representations of numbers in non-integer bases (beta-expansions) using arbitrary real digits, which generalize the usual decimal or binary expansions. For example, a single number can typically be represented by infinitely many distinct beta-expansions. We will introduce so-called quasi-periodic beta-expansions which may be composed of different repeating blocks of digits. Finally, we will formulate a sufficient condition when an extra analog neuron does not bring additional computational power to integer-weight NNs, which is based on quasi-periodic weight parameters whose all beta-expansions are eventually quasi-periodic. We will illustrate the introduced intuitive concepts on numerical examples so that the presentation is accessible to a wide audience.  

Jirí Šíma is a senior scientist at the Institute of Computer Science, The Czech Academy of Sciences, in Prague where he has served in the past as the chair of the Scientific Council and the head of the Department of Theoretical Computer Science. He has lectured and supervised theses at Charles University (associate professor), Czech Technical University in Prague, and Masaryk University in Brno. He has been a program committee member of 30 international conferences, principal investigator of several successful grant projects including tens of researchers, and a member of grant evaluation panels (INTAS Brussels, Czech Science Foundation). His main research interests include neural networks, computational complexity, learning theory, alternative complexity measures, derandomization. He has achieved fundamental outcomes in the theory of neural networks regarding the time complexity of the most common practical learning algorithm---backpropagation and the computational characteristics of continuous, analog, and symmetric models. He has published ca. 100 papers including 2 monographs, 4 book chapters (e.g. MIT Press, Springer), 20 journal papers (e.g. JACM, Neural Computation, Neural Networks, Theoretical Computer Science), 30 papers in conference proceedings (e.g. STOC, LATA, ALT, ICANN, ICANNGA, ICONIP, IJCNN), which have attracted 843 Google Scholar citations with an H-index of 15. He has been awarded Otto Wichterle Award for the monograph on Theoretical Issues of Neural Networks.

More information can be found here.

MSc. Jaroslav Jirkovsky
Senior Application Engineer
HUMUSOFT s.r.o.
International Reseller of MathWorks, Inc., U.S.A.
Czech Republic

Convolutional neural networks are essential tools for deep learning, and are especially useful for image classification, object detection, and recognition tasks. CNNs are implemented as a series of interconnected layers. In MATLAB, you can construct a CNN architecture, train a network, and use the trained network to predict class labels or detect objects using R-CNN, Fast R-CNN and Faster R-CNN object detectors. You can also extract features from a pretrained network, and use these features to train a classifier, or train convolutional neural networks for regression tasks. What the network learns during training is sometimes unclear. Deep Dream is a feature visualization technique in deep learning that synthesizes images that strongly activate network layers. By visualizing these images, you can highlight the image features learned by a network.

More information can be found here.

Assoc. Prof. Julian Miller
Department of Electronics
University of York
United Kingdom

Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999. In its classic form, it uses a very simple integer address-based genetic representation of computational structures in the form of directed graphs. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks, image object detectors, algorithm generation, evolutionary art). CGP has been shown to be comparatively efficient to other GP techniques and is also simple to program. The classical form of CGP has undergone a number of developments which have made it more useful, efficient and flexible in various ways. These include self-modifying CGP (SMCGP), cyclic connections (recurrent-CGP), encoding artificial neural networks and automatically defined functions (modular CGP).

Julian. F. Miller, has a BSc in Physics (Lond), a PhD in Nonlinear Mathematics (City) and a PGCLTHE (Bham) in Teaching. He is a Reader in the Department of Electronics at the University of York. He has chaired or co-chaired sixteen international workshops, conferences and conference tracks in Genetic Programming (GP) and Evolvable Hardware. He is a former associate editor of IEEE Transactions on Evolutionary Computation and an associate editor of the Journal of Genetic Programming and Evolvable Machines and Natural Computing. He is on the editorial board of the journals: Evolutionary Computation, International Journal of Unconventional Computing and Journal of Natural Computing Research. He has publications in genetic programming, evolutionary computation, quantum computing, artificial life, evolvable hardware, computational development, and nonlinear mathematics. He is a highly cited author with over 5,600 citations and over 230 publications in related areas. He has given ten tutorials on genetic programming and evolvable hardware at leading conferences in evolutionary computation. He received the prestigious EvoStar award in 2011 for outstanding contribution to the field of evolutionary computation. He is the inventor of a highly cited method of genetic programming known as Cartesian Genetic Programming and edited the first book on the subject in 2011.

More information can be found here.

Dr. Swagatam Das
Electronics and Communication Sciences Unit
Indian Statistical Institute, Kolkata
India

Cluster analysis aims at the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. On the other hand, image segmentation, the process of partitioning an image into meaningful parts or objects, appears as a fundamental step in many image, video, and computer vision related applications. It is a critical step towards content analysis and interpretation of various types of images such as medical images, satellite images, and natural images. Segmentation is very often carried out based on pixel clustering. This talk will focus on some recent swarm and evolutionary computation based approaches to automatic pattern clustering and image segmentation. In particular the applications of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms will be discussed to clustering, which can be formulated as an optimization problem. We shall also discuss some evolutionary approaches to shape detection from images. Finally the talk will present a few challenging open problems in the context to the so called ‘big data’ clustering and noisy image segmentation.

Swagatam Das is currently serving as an assistant professor at the Electronics and Communication Sciences Unit of the Indian Statistical Institute, Kolkata, India. His research interests include evolutionary computing, pattern recognition, multi-agent systems, and wireless communication. Dr. Das has published one research monograph, one edited volume, and more than 200 research articles in peer-reviewed journals and international conferences. He is the founding co-editor-in-chief of “Swarm and Evolutionary Computation”, an international journal from Elsevier. He also serves as the associate editors of the IEEE Trans. on Systems, Man, and Cybernetics: Systems, IEEE Computational Intelligence Magazine, IEEE Access, Neurocomputing (Elsevier). He is an editorial board member of Progress in Artificial Intelligence (Springer), PeerJ Computer Science, International Journal of Artificial Intelligence and Soft Computing, and International Journal of Adaptive and Autonomous Communication Systems. Dr. Das has 7000+ Google Scholar citations and an H-index of 40 till date. He has been associated with the international program committees and organizing committees of several regular international conferences including IEEE CEC, GECCO, and SEMCCO. He has acted as guest editors for special issues in journals like IEEE Transactions on Evolutionary Computation. He is the recipient of the 2012 Young Engineer Award from the Indian National Academy of Engineering (INAE).

More information can be found here.

Assoc. Prof. Wolfram Wiesemann
KPMG Centre for Advanced Business Analytics
Imperial College Business School, London
United Kingdom

Traditional optimization models only involve deterministic parameters whose values are assumed to be known precisely. However, many practical decision problems involve uncertain parameters such as future prices and resource availabilities. It has been shown that treating these parameters as deterministic quantities can lead to severely suboptimal or even infeasible decisions. Stochastic programming overcomes this deficiency by faithfully treating the uncertain parameters as random variables.
Stochastic programs are usually solved by approximating the realizations of the random variables with a finite number of scenarios. Such scenario-based approaches suffer from a curse of dimensionality, that is, the optimization models scale exponentially with the number of uncertain parameters. In this talk, we survey recent scenario-free approximations to stochastic programming. Instead of discretising the random variables, these approximations employ low-dimensional representations of the problem’s decision variables. The resulting optimization models scale polynomially with the number of uncertain parameters and are thus computationally tractable. We illustrate the computational behavior of these techniques in the context of operations management.

Wolfram Wiesemann is an Associate Professor at the Imperial College Business School and a Fellow of the KPMG Centre for Advanced Business Analytics. He has been a visiting researcher at the Institute of Statistics and Mathematics at Vienna University of Economics and Business, the Computer-Aided Systems Laboratory at Princeton University, the Automatic Control Group at ETH Zurich and the Industrial Engineering and Operations Research Department at Columbia University. He holds a Joint Masters Degree in Management and Computing from Darmstadt University of Technology and a PhD in Operations Research from Imperial College. His current research focuses on the development of tractable computational methods for the solution of stochastic and robust optimization problems, as well as applications in energy systems and operations management.

More information can be found here.

Prof. René Lozi
Laboratoire J.A. Dieudonné
Université de Nice Sophia-Antipolis
France

The tremendous development of new IT technologies, e-banking, e-purchasing, Internet of Things, etc. nowadays increases incessantly the needs for new and more secure cryptosystems. They are used for information encryption, pushing forward the demand for more efficient and secure pseudo-random number generators in the scope of chaos based cryptography. Indeed, chaotic maps show up as perfect candidates able to generate independent and secure pseudo-random sequences (used as information carriers or directly involved in the process of encryption/decryption). We explore several topologies of network of 1-D coupled chaotic mapping (mainly tent map and logistic map) in order to obtain good Chaotic Pseudo Random Number Generators (CPRNG). We focus first on two-dimensional networks. Two topologies are studied: TTLRC non-alternative, and TTLSC alternative. In this case, those networks are equivalent to 2-D maps which achieve excellent random properties and uniform density in the phase plane, thus guaranteeing maximum security when used for chaos based cryptography.
Moreover an extra new nonlinear CPRNG: MTTLSC is proposed. In addition, we explore topologies in higher dimension and the proposed ring coupling with injection mechanism enables us to achieve the strongest security requirements.

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.

Prof. Eva Matalová
Centrum Mendelianum
Czech Republic

Johann Gregor MENDEL (1822 – 1884) became world famous as a scientist, however, he was a truly miltifaceted personality. Mendel´s strong scientific background was built on physics, he was a Ch. Doppler´s student at the Institute of Experimental Physics at the University of Vienna in 1851-3, and also a teacher of physics at the technical high school in Brno 1854 – 1868. Additionally, Mendel was the pioneer of the statistical methods in the study of heredity. Mendel´s use of statistics in his now famous discovery was an exception quite independent of the academic tradition. Gregor Mendel was able to demonstrate in the garden pea the statistical regularities of inheritance which have since been verified. 150 years ago, on Feb 8 and March 8, 1865, Mendel published his famous work Experiments in Plant Hybrids in the form of a two-part lecture for the Nature Research Society (Naturforchender Verein) in Brno and as a printed paper in the Society´s journal a year later.

Eva Matalová is a research scientist at the Institute of Animal Physiology and Genetics of the Academy of Sciences. She is also a Professor at the University of Veterinary and Pharmaceutical Sciences in Brno. Eva Matalova cooperates more than 20 years with Mendelianum of the Moravian Museum. She is a scientific guarantee of several Mendel related projects and one of the key authors of the modern Centrum Mendelianum, covering the scientific and visitor centres, as well as Mendel´s interactive school of genetics. Eva Matalova spent several years abroad, particularly at the King´s College London and she further develops international collaboration not only in her research area but also in Mendel networking.

More information can be found here.

Prof. Yaochu Jin
Department of Computing (Head of NICE group)
University of Surrey
United Kingdom

Morphogenesis is the biological process in which a fertilized cell proliferates, producing a large number of cells that interact with each other to generate the body plan of an organism. Biological morphogenesis, governed by gene regulatory networks through cellular and molecular interactions, can be seen as a self-organizing process. This talk presents a methodology that uses genetic and cellular mechanisms inspired from biological morphogenesis to self-organize swarm robots for adaptive pattern formation in changing environments. We show that the morphogenetic approach is able to self-organize swarm robots without a centralized control, which is nevertheless able to generate predictable global behaviors.

Professor Yaochu Jin is Professor of Computational Intelligence and heads the Nature Inspired Computing and Engineering (NICE) Group , Department of Computing, University of Surrey, Guildford, Surrey, UK. He was Principal Scientist and Group Leader at the Honda Research Institute Europe, Germany before he was appointed Chair in Computational Intelligence at University of Surrey in June 2010. He has published over 150 journal and conference papers and was granted 7 US/EU/Japan patents. His papers have reported over 6,000 citations. Since June 2010, he has successfully attracted funding from EU FP7, UK EPSRC, and industries, including Santander, Bosch UK, HR Wallingford, Intellas UK Ltd, Aero Optimal and Honda. Professor Jin is Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015) and an IEEE Distinguished Lecturer. He currently serves as an Associate Editor of BioSystems, the IEEE Transactions on Cybernetics, the IEEE Transactions on Nanobioscience, IEEE Computational Intelligence Magazine, the Soft Computing Journal and the International Journal of Fuzzy Systems. He is an Editorial Board Member of the Evolutionary Computation Journal (MIT Press).

More information can be found here.

Dr. Thomas Jansen
Dept. of Computer Science
Aberystwyth University
United Kingdom

Evolutionary algorithms and other randomised search heuristics are often used to tackle difficult optimisation problems. In this context the most important question we want to see answered by an analysis of these algorithms is that of their efficiency. How well does a specific heuristic perform on a specific problem? In the last 20 years the theory of evolutionary algorithms has developed run time analysis to answer a specific version of this question. How long does it take a specific heuristic on average to find an optimal solution of a specific problem? There is a large number of impressing successes, developing analytical tools and applying them to increasingly complex heuristics and problems. However, it is not clear how practitioners in the field have benefitted from these developments. One can argue that evolutionary algorithm theory has concentrated on an aspect of performance that is at odds with real applications of evolutionary algorithms. A rather novel perspective, fixed budget computations, are a better match with the way evolutionary algorithms are applied. The talk provides an overview of the state of the art in evolutionary algorithm theory, introduces fixed budget computations and explains how results from run time theory can systematically be transformed into results of this new and more useful type.

Thomas Jansen is Senior Lecturer at the Department of Computer Science at Aberystwyth University, Wales, UK (since January 2013). He studied Computer Science at the University of Dortmund, Germany, and received his diploma and Ph.D. there. From September 2001 to August 2002 he stayed as a Post-Doc at Kenneth De Jong's EClab at the George Mason University in Fairfax, VA. He has published 19 journal papers, 40 conference papers, contributed seven book chapters and authored one book on evolutionary algorithm theory. His research is centred around design and theoretical analysis of artificial immune systems, evolutionary algorithms and other randomised search heuristics. He is associate editor of Evolutionary Computation (MIT Press) and Artificial Intelligence (Elsevier), member of the steering committee of the Theory of Randomised Search Heuristics workshop series and co-track chair of the Genetic Algorithm track of GECCO 2013 and 2014. In 2015 he will be co-organising FOGA 2015.

More information can be found here.

Assoc. Prof. Ponnuthurai Nagaratnam Suganthan
School of Electrical and Electronic Engineering
Nanyang Technological University
Singapore

Differential evolution has recently become a (or the most) competitive real-parameter optimizer in diverse scenarios. This talk will present some important parameter and operator adaptation methods used with differential evolution currently. The talk will also touch on a few different optimization problem scenarios such as single objective, multiobjective, dynamic, multimodal, etc. The talk will also identify some future research directions.

Ponnuthurai Nagaratnam Suganthan received the B.A degree and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK. He obtained his Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He is an Editorial Board Member of the Evolutionary Computation Journal, MIT Press. He is an associate editor of the IEEE Trans on Cybernetics (formerly IEEE T-SMC-B), IEEE Trans on Evolutionary Computation, Information Sciences (Elsevier), Pattern Recognition (Elsevier). He is a founding co-editor-in-chief of Swarm and Evolutionary Computation, an Elsevier Journal. SaDE (April 2009) paper won "IEEE Trans. on Evolutionary Computation" outstanding paper award in 2012. His research interests include evolutionary computation, pattern recognition, multi-objective evolutionary algorithms, applications of evolutionary computation and neural networks. His publications have been well cited (Googlescholar Citations: 11k). His SCI indexed publications attracted over 1000 SCI citations in calendar year 2013 alone. He is a Senior Member of the IEEE and an elected AdCom member of the IEEE CIS (2014-2016).

More information can be found here.

Prof. René Lozi
Laboratoire J.A. Dieudonné
Université de Nice Sophia-Antipolis
France

During the last decades, on one hand, it had been highlighted the duality between chaotic numbers and pseudo-random numbers (e.g. sometimes chaotic numbers used in particle swam optimisation are more efficient than pseudo-random numbers, sometimes high quality pseudo-random numbers are needed for cryptography). On the other hand, emergence of pseudo-randomness from chaos via various under-sampling methods has been recently discovered. Instead of opposing both qualities (chaos and pseudo-randomness) of numbers, it should be more interesting to shape mixed Chaotic/Pseudo-random number generators, which can modulate the desired properties between chaos and pseudo-randomness. Because nowadays there exist increasing demands for new and more efficient number generators of this type (these demands arise from different applications, such as multi-agents competition, global optimisation via evolutionary algorithms or secure information transmission ,etc.), it is important to develop new tools to shape more or less automatically various families of such generators. Mathematical chaotic circuits have been recently introduced for such a purpose. By analogy of electronic circuitry: i;e. the design of electronic circuits which are composed of individual electronic components, such as resistors, transistors, capacitors, inductors and diodes, connected by conductive wires through which electric current can flow; mathematical chaotic circuits are composed of individual components (generators, couplers, samplers, mixers, and reducers, ...) connected through streams of data. The combination of such mathematical components leads to several news applications such as improving the performance of well known chaotic attractors (Hénon, Chua, Lorenz, Rössler,...). They can be also used in larger scale to shape numerous architectures of mixed Chaotic/Pseudo Random Number Generators, as we will show in this talk.

Professor René Lozi received his Ph.D. 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. He has served as the Director of IUFM (2001-2006) and as Vice-Chairman of the French Board of Directors of IUFM (2004-2006). He is member of the Editorial Board of Indian J. of Indust. and Appl. Maths and J. of Nonlinear Systems and Appl., and member of the Honorary Editorial Board of Intern. J. of Bifurcation and 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. He is working in this field with renowned researchers, such as Professors Leon O. Chua (inventor of "Chua circuit") and Alexander Sharkovsky (who introduced the "Sharkovsky's order").

More information can be found here.

Prof. Riccardo Poli
School of Computer Science and Electronic Engineering
University of Essex
United Kingdom

The study of complex adaptive systems is among the key modern tasks in science. Such systems show radically different behaviours at different scales and in different environments, and mathematical modelling of such emergent behaviour is very difficult, even at the conceptual level. We require a new methodology to study and understand complex, emergent macroscopic phenomena. Coarse graining, a technique that originated in statistical physics, involves taking a system with many microscopic degrees of freedom and finding an appropriate subset of collective variables that offer a compact, computationally feasible description of the system, in terms of which the dynamics looks “natural”. This talk will present the key ideas of the approach and will show how it can be applied to evolutionary dynamics.

Riccardo Poli is a professor in the School of Computer Science and Electronic Engineering at Essex, UK. His research interests include genetic programming, particle swarm optimisation, the theory of evolutionary algorithms, and brain-computer interfaces. He was a recipient of the Evo* award for outstanding contributions to the field of evolutionary computation. He has published over 300 refereed papers on evolutionary algorithms, biomedical engineering, neural networks and image/signal processing. He has co-authored the books Foundations of Genetic Programming, Springer, 2002 and A Field Guide to Genetic Programing, Lulu, 2008. He has been chair of numerous international conferences. He is an advisory board member of the Evolutionary Computation journal and an associate editor of the Genetic Programming and Evolvable Machines and of Swarm Intelligence journals.

More information can be found here.

RNDr. Věra Kůrková, DrSc.
Institute of Computer Science
Academy of Sciences of the Czech Republic
Czech Republic

Originally, artificial neural networks were built from biologically inspired units called perceptrons. Later, other types of units became popular in neurocomputing due to their good mathematical properties. Among them, radial-basis-function (RBF) units and kernel units became most popular. The talk will discuss advantages and limitations of networks with these two types of computational units. Higher flexibility in choice of free parameters in RBF will be compared with benefits of geometrical properties of kernel models allowing applications of maximal margin classification algorithms, modelling of generalization in learning from data in terms of regularization, and characcterization of optimal solutions of learning tasks. Critical influence of input dimension on behavior of these two types of networks will be described. General results will be illustrated by the paradigmatic examples of Gaussian kernel and radial networks.

RNDr. Věra Kůrková, DrSc., received the Ph.D. in topology from Charles University, Prague. Since 1990, she has been a scientist with the Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague. From 2002 to 2008, she was the Head of the Department of Theoretical Computer Science. Her research interests include mathematical theory of neurocomputing and softcomputing, machine learning, and nonlinear approximation theory. In 2010 she was awarded by the Czech Academy of Sciences the Bolzano Medal for her contributions to mathematics. She is a member of the Editorial Boards of the journals Neural Networks, Neural Processing Letters, and in 2008-2009 also of IEEE Transactions on Neural Networks. She is a member of the Board of the European Neural Networks Society and was the general chair of conferences ICANNGA 2001 and ICANN 2008.

More information can be found here.

Prof. Alex Gammerman
Co-Director of the Computer Learning Research Centre
Royal Holloway and Bedford New College, University of London
United Kingdom

The talk describes a new machine learning technique called Conformal Predictors. The technique is based on recently developed computable approximations of Kolmogorov’s algorithmic notion of randomness, and it allows us to make reliable predictions using valid measures of confidence in both ”batch” and ”online” modes of learning. The advantages are as follows:

• it can control the number of erroneous predictions by selecting a suitable confidence level;
• unlike many conventional techniques the approach does not make any additional assumption about the data beyond the iid assumption: the examples are independent and identically distributed;
• it allows to make estimation of confidence of the prediction for individual examples;
• can be used as a region predictor with a number of possible predicted values;
• can be used in high-dimensional problems where number of attributes greatly exceeds the number of objects;
• it gives well-calibrated predictions that can be used in online and off-line learning as well as in ”intermediate” types of learning e.g. ”slow”, ”lazy”.

Professor Gammerman was appointed to the established Chair in Computer Science at University of London (Royal Holloway and Bedford New College) in 1993 and served as Head of the Computer Science Department from 1995 until 2005. He is also Founding Director of the Computer Learning Research Centre at Royal Holloway, University of London. Professor Gammerman is a Fellow of the Royal Statistical Society, Fellow of the Royal Society of Arts, Fellow of British Computer Society. He chaired and participated in organising committees of many international conferences and workshops on Machine Learning and Bayesian methods in Europe, Russia and in the United States. Professor Gammerman's current research interest lies in field of Algorithmic Randomness Theory with its applications to machine learning and conformal predictors. Areas in which these techniques have been applied include medical diagnosis, forensic science, genomics, proteomics and environment. Professor Gammerman has published over a hundred research papers and several books on computational learning and probabilistic reasoning.

More information can be found here.

Dr. William B. Langdon
Research Fellow
University College London
United Kingdom

Evolutionary computing, particularly genetic programming, can optimise software and software engineering, including evolving test benchmarks, search meta-heuristics, protocols, composing web services, improving hashing and garbage collection, redundant programming and even automatically fixing bugs. Often there are many potential ways to balance functionality with resource consumption. But a human programmer cannot try them all. Also the optimal trade off may be different on each hardware platform and it vary over time or as usage changes. It may be genetic programming can automatically suggest different trade offs for each new market.

Dr. Langdon worked in the power supply industries and as a software consultant before returning to university to gain a PhD on evolving software with genetic programming. Bill has worked both on application and theoretical foundations of GP and has written three books on GP () and given presentations in five continents. Applications include scheduling, e-commerce, data mining, evolving combinations of classifiers (MCS), swarm systems (PSO) and Bioinformatics (eg non-human contaminants in the human genome databases). Whilst theory includes GP schema theory, markov analysis, the halting probability and elementary fitness landscapes. Recent work includes using grammar based GP to re-engineer nVidia CUDA software.

More information can be found at  here.

Dr. Xin-She Yang
Senior Research Scientist
National Physical Laboratory
United Kingdom

Nowadays, nature-inspired metaheuristics have become an integrated part of soft computing and computational intelligence, and they have been applied to solve a wide range of tough optimization problems. Seemingly simple algorithms can often deal with complex, even NP-hard, optimization problems with surprisingly good performance and results. In this talk, I will review some of the recent metaheuristic algorithms such as firefly algorithm and cuckoo search and their differences from particle swarm optimization and other metaheuristics. We will try to analyse the key components of metaheuristic methods in terms of convergence and search characteristics. We will also give a few examples in real-world applications and suggest some open problems for further research.

Dr Xin-She Yang received his DPhil in Applied Mathematics from University of Oxford, then worked at Leeds University and Cambridge University for a few years, and now is a Senior Research Scientist at National Physical Laboratory. He is a Guest Professor at Shandong University and Harbin Engineering University. He has authored a dozen books and published more than 110 papers. He is the Editor-in-Chief of Int. J. Mathematical Modelling and Numerical Optimisation (IJMMNO, Inderscience), serves as an editorial board member of several international journals, including Elsevier's Journal of Computational Science (JoCS). He is also the vice chair of the IEEE CIS task force on business intelligence and knowledge management.

More information can be found at here.

Prof. Daniela Zaharie
Faculty of Mathematics and Computer Science
West University of Timisoara
Romania

Differential Evolution (DE) is currently one of the most used population based stochastic metaheuristic. Its popularity is mainly due to its simplicity and effectiveness in solving various types of problems, including multi-objective, multi-modal, dynamic and constrained optimization problems. Since Rainer Storn and Kenneth Price proposed the first DE versions, more than fifteen years ago, dozens of differential evolution flavors, involving changes in the main operators, hybridization with other optimization methods, automated parameters tuning, (self)-adaptation schemes, structured populations and so on, have been proposed.
Despite the large number of reported applications of DE and of the huge volume of experimental results it is still difficult to give answers to questions like "Why is DE successful for a class of problems and why does it fail for other problems?" or "How could we control the DE behavior?". The theoretical analysis of DE is still well behind the experimental results, most of the current knowledge on differential evolution being based on empirical observations.
This presentation will review the existing theoretical results concerning the convergence properties of DE and the influence of the choice of DE parameters on the population evolution and will focus on the usage of these results in deriving practical insights for designing effective and efficient optimization tools.

Prof. Daniela Zaharie received her PhD degree in Probability and Statistics at the West University of Timisoara, Romania in 1997 and currently is professor at the Department of Computer Science of the same university. Her main research interests are: evolutionary computing, machine learning, data mining, statistical modelling, image processing and high performance computing.

More information can be found at here.

Prof. Jiri Bila
Department of Instrumentation and Control Engineering
Czech Technical University in Prague
Czech Republic

The proposed lecture maps the emergent phenomena in the following fields:
  – In the evolution of natural shapes and processes.
  – In multi agent systems.
  – In the behavior of the ant colonies.
  – In urban and non urban development of large cities.
  – In conceptual design.
  – In problem solving.

The lecture contains a part of the essential and breakthrough opinions to the emergence and emergent phenomena till nowadays. It introduces Complex Systems as one of the field appropriate for investigation of conditions of emergent phenomena initiation. Principal obstacles that obstruct a real investigation of emergent phenomena in the level of nowadays analytical science are introduced. The cases of emergent phenomena in problem solving are illustrated.

Prof. Jiri Bila finished his study on Faculty of mechanical engineering, CTU Prague in 1969. His CSc. thesis in the field of Technical Cybernetics he defended in 1977, and in 1987 he achieved the degree DrSc in the same field of science. He is currently full professor of Technical Cybernetics in Faculty of Mechanical Engineering, CTU in Prague, since 1989. The kernel of his scientific activities is in analysis of non-linear systems with deterministic chaos, in application of artificial intelligence and neural networks for biosignals and in intelligent methods in diagnostics. He is author and co-author of 17 grant projects in various levels 1991-2011, author/co-author of 5 books and over 250 journal and conference papers.

More information can be found at here.