Schedule of tutorials - 19th July, 2020

11:30 - 13:30
14:00 - 16:00
16:30 - 18:30
19:00 - 21:00
11:30 - 13:30
Tutorial Title Presenter Conference Contact Email
RANKING GAME: How to combine human and computational intelligence? Peter Erdi WCCI Peter.Erdi@kzoo.edu
Adversarial Machine Learning: On The Deeper Secrets of Deep Learning Danilo V. Vargas IJCNN vargas@inf.kyushu-u.ac.jp
From brains to deep neural networks Saeid Sanei, Clive Cheong Took IJCNN Clive.CheongTook@rhul.ac.uk
Deep randomized neural networks Claudio Gallicchio, Simone Scardapane IJCNN gallicch@di.unipi.it
Brain-Inspired Spiking Neural Network Architectures for Deep, Incremental Learning and Knowledge Evolution       Nikola Kasabov IJCNN nkasabov@aut.ac.nz
Fundamentals of Fuzzy Networks Alexander Gegov, Farzad Arabikhan FUZZ alexander.gegov@port.ac.uk
Pareto Optimization for Subset Selection: Theories and Practical Algorithms Chao Qian, Yang Yu CEC qianc@lamda.nju.edu.cn
Selection Exploration and Exploitation Stephen Chen, James Montgomery CEC sychen@yorku.ca
Benchmarking and Analyzing Iterative Optimization Heuristics with IOHprofiler Carola Doerr, Thomas Bäck, Ofer Shir, Hao Wang CEC h.wang@liacs.leidenuniv.nl
Computational Complexity Analysis of Genetic Programming Pietro S. Oliveto, Andrei Lissovoi         CEC p.oliveto@sheffield.ac.uk
Self-Organizing Migrating Algorithm - Recent Advances and Progress in Swarm Intelligence Algorithms

Roman Senkerik CEC senkerik@utb.cz
Visualising the search process of EC algorithms Su Nguyen, Yi Mei, and Mengjie Zhang CEC P.Nguyen4@latrobe.edu.au
14:00 - 16:00
Tutorial Title Presenter Conference Contact Email
Instance Space Analysis for Rigorous and Insightful Algorithm Testing Kate Smith-Miles, Mario Andres, Munoz Acosta WCCI munoz.m@unimelb.edu.au
Advances in Deep Reinforcement Learning Thanh Thi Nguyen, Vijay Janapa Reddi,Ngoc Duy Nguyen, IJCNN thanh.nguyen@deakin.edu.au 
Machine learning  for data streams in Python with scikit-multi flow Jacob Montiel, Heitor Gomes,Jesse Read, Albert Bifet IJCNN heitor.gomes@waikato.ac.nz
Deep Learning for Graphs Davide Bacchiu IJCNN bacciu@di.unipi.it
Explainable-by-Design Deep Learning: Fast, Highly Accurate, Weakly Supervised, Self-evolving Plamen Angelov IJCNN p.angelov@lancaster.ac.uk
Cartesian Genetic Programming and its Applications Lukas Sekanina, Julian Miller CEC sekanina@fit.vutbr.cz
Large-Scale Global Optimization Mohammad Nabi Omidvar, Daniel Molina CEC M.N.Omidvar@leeds.ac.uk 
Niching Methods for Multimodal Optimization Mike Preuss, Michael G. Epitropakis, Xiadong Li CEC m.epitropakis@gmail.com
A Gentle Introduction to the Time Complexity Analysis of Evolutionary Algorithms     Pietro S. Oliveto CEC p.oliveto@sheffield.ac.uk
Theoretical Foundations of Evolutionary Computation for Beginners and Veterans. Darrel Whitely CEC darrell.whitley@gmail.com
Evolutionary Computation for Dynamic Multi-objective Optimization Problems Shengxiang Yang CEC syang@dmu.ac.uk
16:30 - 18:30
Tutorial Title Presenter Conference Contact Email
New and Conventional Ensemble Methods José Antonio Iglesias, María Paz Sesmero Lorente, Araceli Sanchis de Miguel WCCI jiglesia@inf.uc3m.es
Evolution of Neural Networks Risto Miikkulainen IJCNN risto@cs.utexas.edu
Mechanisms of Universal Turing Machines in Developmental Networks for Vision, Audition, and Natural Language Understanding Juyang Weng IJCNN juyang.weng@gmail.com
Generalized constraints for knowledge-driven-and-data-driven approaches Baogang Hu IJCNN hubaogang@gmail.com
Randomization Based Deep and Shallow Learning Methods for Classification and Forecasting P N Suganthan IJCNN EPNSugan@ntu.edu.sg
Using intervals to capture and handle uncertainty Chritian Wagner, Prof. Vladik Kreinovich, Dr Josie McCulloch, Dr Zack Ellerby FUZZ Christian.Wagner@nottingham.ac.uk
Fuzzy Systems for Neuroscience and Neuro-engineering Applications Javier Andreu, CT Lin FUZZ javier.andreu@essex.ac.uk
Evolutionary Algorithms and Hyper-Heuristics Nelishia Pillay CEC npillay@cs.up.ac.za
Recent Advances in Particle Swarm Optimization Analysis and Understanding Andries Engelbrecht, Christopher Cleghorn CEC engel@sun.ac.za
Recent Advanced in Landscape Analysis for Optimisation Katherine Malan, Gabriela Ochoa CEC malankm@unisa.ac.za
Evolutionary Machine Learning  Masaya Nakata, Shinichi Shirakawa, Will Browne CEC nakata-masaya-tb@ynu.ac.jp
Evolutionary Many-Objective Optimization Hisao Ishibuchi, Hiroyuki Sato CEC h.sato@uec.ac.jp
19:00 - 21:00
Tutorial Title Presenter Conference Contact Email
Multi-modality Helps in Solving Biomedical Problems: Theory and ApplicationsSriparna Saha, Pratik DuttaWCCIpratik.pcs16@iitp.ac.in
Probabilistic Tools for Analysis of Network Performance Věra Kůrková IJCNN vera@cs.cas.cz
Experience Replay for Deep Reinforcement Learning ABDULRAHMAN ALTAHHAN, VASILE PALADE IJCNN A.Altahhan@leedsbeckett.ac.uk
PHYSICS OF THE MIND Leonid I. Perlovsky IJCNN lperl@rcn.com
Deep Stochastic Learning and Understanding Jen-Tzung Chien IJCNN jtchien@nctu.edu.tw
Paving the way from Interpretable Fuzzy Systems to Explainable AI Systems José M. Alonso, Ciro Castiello, Corrado Menca, Luis Magdalena FUZZ josemaria.alonso.moral@usc.es
Evolving neuro-fuzzy systems in clustering and regression Igor Škrjanc, Fernando Gomide, Daniel Leite, Sašo Blažič FUZZ Igor.Skrjanc@fe.uni-lj.si
Differential Evolution Rammohan Mallipeddi,  Guohua Wu CEC mallipeddi.ram@gmail.com
Bilevel optimization Ankur Sinha, Kalyanmoy Deb CEC asinha@iima.ac.in
Nature-Inspired Techniques for Combinatorial Problems Malek Mouhoub

CEC Malek.Mouhoub@uregina.ca
Dynamic Parameter Choices in Evolutionary Computation Carola Doerr, Gregor Papa  CEC gregor.papa@ijs.si
Evolutionary computation for games: learning, planning, and designing

Julian Togelius , Jialin Liu 

CEC liujl@sustech.edu.cn

RANKING GAME: How to combine human and computational intelligence?
(A Cross-disciplinary tutorial)

Organizer: Péter Érdi (Henry Luce Professor of Complex Systems Studies, Kalamazoo College;

Wigner Research Centre for Physics, Budapest
perdi@kzoo.edu
http://people.kzoo.edu/~perdi/

Goal:

Comparison, ranking and even rating is a fundamental feature of human nature. The goal of this tutorial to explain the integrative aspects of the evolutionary, psychological, institutional and algorithmic aspects of ranking. Since we humans (1) love lists; (2), are competitive and (3), are jealous of other people, we like ranking. The practice of ranking is studied in social psychology and political science, the algorithms of ranking in computer science. Initial results of the possible neural and cognitive architectures behind rankings are also reviewed.

The tutorial is based on the book of the organizer:

Ranking:

The Unwritten Rules of the Social Game We All Play, Oxford University Press, 2020
https://global.oup.com/academic/product/ranking-9780190935467?cc=us&lang=en

Tentative plan:
1. Why we rank? How we rank?
1.1 Comparison, ranking and rating
1.2 The social psychology of ranking
1.3 Biased ranking
1.4 Social ranking
Social ranking in animal societies
Pecking order
1.5 Struggle for reputation
2. Ranking in the every day’s life
2.1 Ranking countries
2.2 Ranking universities
3. A success story: ranking the web
3.1 PageRank and its variations
3.2. Rank reversal
3.3 Webometrics
4. Scientific journals and scientists
4.1 Publish and perish, but where? Impact factor, the fading superstar
4.2 h-index and its variations
5. Cognitive architectures for ranking: are lists perfectly designed for our brain?
6. Ranking, rating and everything else. The mystery of the future: how to combine
human and computational intelligence?

Bio:

Dr. Péter Érdi serves as the Henry R. Luce Professor of Complex Systems Studies at Kalamazoo College. He is also a research professor in his home town, in Budapest, at the Wigner Research Centre of Physics. In addition, he is the founding co-director of the Budapest Semester in Cognitive Science, a study abroad program. Péter is a Member of the Board of Governors of the International Neural Network Society, the past Vice President of the International Neural Network Society, and among others as the past Editor-in-Chief of Cognitive Systems Research. He served as the Honorary Chair of the IJCNN 2019, and now serving as an IJCNN Technical Chair of the IEEE World Congress on Computational Intelligence in Glasgow. His books on mathematical modeling of chemical, biological, and other complex systems have been published by Princeton University Press, MIT Press, Springer Publishing house. His new book RANKING: The Unwritten Rules of the Social Game We All Play was published recently by the Oxford University Press, and is already under translation for several languages. See also aboutranking.com

Instance Space Analysis for Rigorous and Insightful Algorithm Testing

Prof. Kate Smith-Miles
School of Mathematics and Statistics, The University of Melbourne, Australia
Email: kate.smithmiles@gmail.com

Dr. Mario Andrés Muñoz
School of Mathematics and Statistics, The University of Melbourne, Australia
Email: munoz.m@unimelb.edu.au

Abstract

Algorithm testing often consists of reporting on-average performance across a suite of well-studied benchmark instances. Therefore, the conclusions drawn from testing depend on the choice of benchmarks. Hence, test suites should be unbiased, challenging, and contain a mix of synthetic and real-world-like instances with diverse structure. Without diversity, the conclusions drawn future expected algorithm performance are necessarily limited. Moreover, on-average performance often disguises the strengths and weaknesses of an algorithm for particular types of instances. In other words, the standard benchmarking approach has two limitations that affect the conclusions: (a) there is no mechanism to assess whether the selected test instances are unbiased and diverse enough; and (b) there is little opportunity to gain insights into the strengths and weaknesses of algorithms, when hidden by on-average performance metrics.

This tutorial introduces Instance Space Analysis (ISA), a visual methodology for algorithm evaluation that reveals the relationships between the structure of an instance and its impact on performance. ISA offers an opportunity to gain more nuanced insights into algorithm strengths and weaknesses for various types of test instances, and to objectively assess the relative power of algorithms, unbiased by the choice of test instances. A space is constructed whereby an instance is represented a point in a 2d plane, with algorithm footprints being the regions of predicted good performance of an algorithm, based on statistical evidence. From this broad instance space, we can assess the diversity of a chosen test set. Moreover, through ISA we can identify regions where additional test instances would be valuable to support greater insights. By generating new instances with controllable properties, an algorithm can be “stress-tested” under all possible conditions. The tutorial makes use of the on-line tools available at the Melbourne Algorithm Test Instance Library with Data Analytics (MATILDA) and provides access to its MATLAB computational engine. MATILDA also provides a collection of ISA results and other meta-data available for downloading for several well-studied problems from optimization and machine learning, from previously published studies.

Tutorial Presenters (names with affiliations):

Prof. Kate Smith-Miles
School of Mathematics and Statistics
The University of Melbourne, Australia

Dr. Mario Andrés Muñoz
School of Mathematics and Statistics
The University of Melbourne, Australia

Tutorial Presenters’ Bios:

Kate Smith-Miles is a Professor of Applied Mathematics in the School of Mathematics and Statistics at The University of Melbourne and holds a five year Laureate Fellowship from the Australian Research Council. Prior to joining The University of Melbourne in September 2017, she was Professor of Applied Mathematics at Monash University, and Head of the School of Mathematical Sciences (2009-2014). Previous roles include President of the Australian Mathematical Society (2016-2018), and membership of the Australian Research Council College of Experts (2017-2019). Kate was elected Fellow of the Institute of Engineers Australia (FIEAust) in 2006, and Fellow of the Australian Mathematical Society (FAustMS) in 2008.

Kate obtained a B.Sc(Hons) in Mathematics and a Ph.D. in Electrical Engineering, both from The University of Melbourne. Commencing her academic career in 1996, she has published 2 books on neural networks and data mining, and over 260 refereed journal and international conference papers in the areas of neural networks, optimization, data mining, and various applied mathematics topics. She has supervised 28 PhD students to completion and has been awarded over AUD$15 million in competitive grants, including 13 Australian Research Council grants and industry awards. MATILDA and the instance space analysis methodology has been developed through her 5-year Laureate Fellowship awarded by the Australian Research Council.

Mario Andrés Muñoz is a Researcher in Operations Research in the School of Mathematics and Statistics, at the University of Melbourne. Before joining the University of Melbourne, he was a Research Fellow in Applied Mathematics at Monash University (2014-2014), and a Lecturer at the Universidad del Valle, Colombia (2008-2009)

Mario Andrés obtained a B.Eng. (2005) and a M.Eng. (2008) in Electronics from Universidad del Valle, Colombia, and a Ph.D. (2014) in Engineering from the University of Melbourne. He has published over 40 refereed journal and conference papers in optimization, data mining, and other inter-disciplinary topics. He has developed and maintains the MATLAB code that drives MATILDA.

External website with more information on Tutorial (if applicable):

https://matilda.unimelb.edu.au/matilda/WCCI2020

Multi-modality for Biomedical Problems: Theory and Applications

 Dr. Sriparna Saha
Associate Professor, Department of Computer Science and Engineering
Indian Institute of Technology Patna
Email:sriparna@iitp.ac.in

Mr. Pratik Dutta
PhD Research Scholar, Department of Computer Science and Engineering
Indian Institute of Technology Patna
Email: pratik.pcs16@iitp.ac.in

Abstract

With the exploration of omics technologies, researchers are able to collect high-throughput biomedical data. The explosion of these new frontier omics technologies produces diverse genomic datasets such as microarray gene expression, miRNA expression, DNA sequence, 3D structures etc. These different representations (modality) of the biomedical data contain distinct, useful and complementary information of different samples. As a consequence, there is a growing interest in collecting ”multi-modal” data for the same set of subjects and integrating this heterogeneous information to obtain more profound insights into the underlying biological system. The current tutorial will discuss in detail different problems of bioinformatics and the concepts of multimodality in bioinformatics. In recent years different machine learning and deep learning based approaches become popular in dealing with multimodal data. Drawing attention from the above facts, this tutorial is a roadmap of existing deep multi-modal architectures in solving different computational biology problems. This tutorial will be an advanced survey equally of interest to academic researchers and industry practitioners – very timely with so much vibrant research in the computational biology domain over the past 5 years. As IEEE WCCI is an prestigious conference for discussion of neural network frontiers, this tutorial is very much relevant for IEEE WCCI.

Tutorial Presenters (names with affiliations): 

  1. SriparnaSaha, Associate Professor, Department of Computer Science and Engineering, Indian Institute of Technology Patna
  2. Pratik Dutta, PhD Research Scholar, Department of Computer Science and Engineering, Indian Institute of Technology Patna

Tutorial Presenters’ Bios: 

  1. SriparnaSaha:SriparnaSaha received the M.Tech and Ph.D. degrees in computer science from Indian Statistical Institute, Kolkata, India, in the years 2005 and 2011, respectively. She is currently an Associate Professor in the Department of Computer Science and Engineering, Indian Institute of Technology Patna, India. Her current research interests include machine learning, multi-objective optimization, evolutionary techniques, text mining and biomedical information extraction. She is the recipient of the Google India Women in Engineering Award, 2008, NASI YOUNG SCIENTIST PLATINUM JUBILEE AWARD 2016, BIRD Award 2016, IEI Young Engineers’ Award 2016, SERB WOMEN IN EXCELLENCE AWARD 2018 and SERB Early Career Research Award 2018. She is the recipient of DUO-India fellowship 2020, Humboldt Research Fellowship 2016, Indo-U.S. Fellowship for Women in STEMM (WISTEMM) Women Overseas Fellowship program 2018 and CNRS fellowship. She had also received India4EU fellowship of the European Union to work as a Post-doctoral Research Fellow in the University of Trento, Italy from September 2010 to January 2011. She was also the recipient of Erasmus Mundus Mobility with Asia (EMMA) fellowship of the European Union to work as a Post-doctoral Research Fellow in the Heidelberg University, Germany from September 2009 to June 2010. She had visited University of Caen, France as a visiting scientist during the period October 2013, December 2013, May-July, 2014 and May-June, 2015; University of Mainz, Germany as a visiting scientist from April-September 2016, April-August 2017; University of Kyoto, Japan as a visiting professor from June-July 2018; University of California, San Diego as a visiting scientist for the period December 2018-February 2019; Dublin City University, Ireland as a visiting scientist in July, 2019. She won the best paper awards in CLINICAL-NLP workshop of COLING 2016, IEEE-INDICON 2015, International Conference on Advances in Computing, Communications and Informatics (ICACCI 2012). According to Google Scholar, his citation count is 3488 and with h-index 24. For more information please refer to : www.iitp.ac.in/ sriparna.
  2. Pratik Dutta:Pratik Dutta is currently working as PhD scholar in the Department of Computer Science and Engineering at Indian Institute of Technology Patna. He received his BE and ME degree from Indian Institute of Engineering Science and Technology, Shibpur in 2013 and 2015, respectively. His research interest lies in computational biology, genomic sequence, protein-protein interaction, machine learning and deep learning techniques. He is the recipient of Visvesvaraya PhD research fellowship, an initiative of Ministry of Electronics and Information Technology (MeitY), Government of India (GoI). According to Google Scholar, his citation count is 34 along with h-index 4. For the last four years, he has extensively explored in various frontiers of computational biology more precisely in protein-protein interaction identification. He has published various research articles in different prestigious fora like Computers in Biology and Medicine, IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Journal of Biomedical and Health Informatics, Nature-Scientific Reports, etc.

External website with more information on Tutorial (if applicable):

https://www.iitp.ac.in/~sriparna/WCCI.html

Venue: With WCCI 2020 being held as a virtual conference, there will be a virtual experience of Glasgow, Scotland accessible through the virtual platform. We hope that everyone will have a chance to visit one of Europe’s most dynamic cultural capitals and the “World’s Friendliest City” soon in the future!.