GECCO 2019 will have a number of competitions ranging from different types of optimization problems to games and industrial problems. If you are interested in a particular competition, please follow the links to their respective web pages (see list below).
|100-Digit Challenge, and Four Other Numerical Optimization Competitions|
|AI Competition for the Legends of the Three Kingdoms Game (三国杀)|
|Bi-Objective Optimisation for the Travelling Thief Problem|
|Black Box Optimization Competition (BBComp)|
|Competition on Niching Methods for Multimodal Optimization|
|Evolutionary Computation in Uncertain Environments: A Smart Grid Application|
|Internet of Things: Online Event Detection for Drinking Water Quality Control|
|Virtual Creatures Competition|
100-Digit Challenge, and Four Other Numerical Optimization Competitions
Research on single objective optimization algorithms often forms the foundation for more complex methods, such as niching algorithms and both multi-objective and constrained optimization algorithms. Traditionally, single objective benchmark problems are also the first test for new evolutionary and swarm algorithms. Additionally, single objective benchmark problems can be transformed into dynamic, niching composition, computationally expensive and many other classes of problems. It is with the goal of better understanding the behavior of evolutionary algorithms as single objective optimizers that we are introducing the 100-Digit Challenge.
The original SIAM 100-Digit Challenge was developed in 2002 by Oxford’s Nick Trefethen in conjunction with the Society for Industrial and Applied Mathematics (SIAM) as a test for high-accuracy computing. Specifically, the challenge was to solve 10 hard problems to 10 digits of accuracy. One point was awarded for each correct digit, making the maximum score 100, hence the name. Contestants were allowed to apply any method to any problem and take as long as needed to solve it. Out of the 94 teams that entered, 20 scored 100 points and 5 others scored 99.
Like the SIAM version, our competition has 10 problems, which in our case are 10 functions to optimize, and the goal is to compute each function’s minimum value to 10 digits of accuracy without being limited by time. In contrast to the SIAM version, however, our 100-Digit Challenge asks contestants to solve all ten problems with one algorithm, although limited control parameter “tuning” for each function will be permitted to restore some of the original contest’s flexibility. Another difference is that the score for a given function is the average number of correct digits in the best 25 out of 50 trials (still a maximum of 10 points per function).
The details of other competitions on many-/multi-objective, constrained single objective and single objective expensive problems can be found at the official webpage given below.
Submission deadline for 2-page paper: April 3
Review: April 13
Notification: April 17
Camera-ready: April 24
Submission deadline:April 03, 2019
Kenneth V. Price earned his B.Sc. in physics from Rensselaer Polytechnic Institute in 1974. He briefly worked as a supervisor at the Teledyne-Gurley Scientific Instrument Company in Troy, New York before moving to San Francisco. He currently resides in Vacaville, California. An avid hobbyist, he is self-taught in the field of evolutionary computation. In 1994, he published an early ensemble annealing, threshold accepting algorithm ("genetic annealing"), which led Dr. R. Storn to challenge him to solve the Chebyshev polynomial fitting problem. Ken’s discovery of differential mutation proved to be the key to solving not only the Chebyshev polynomial fitting problem, but also many other difficult numerical global optimization problems. He is co-author of both the seminal paper on the differential evolution algorithm and the book “Differential Evolution: A practical approach to global optimization”. In 2017, Ken was awarded the IEEE CIS Pioneer Award for his seminal work on the differential evolution algorithm. Ken has also authored or coauthored seven additional peer-reviewed papers, contributed chapters to three books on optimization and has served as a reviewer for twelve different journals.
Ponnuthurai Nagaratnam Suganthan
Ponnuthurai Nagaratnam Suganthan received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. After completing his PhD research in 1995, he served as a pre-doctoral Research Assistant in the Dept of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Dept of Computer Science and Electrical Engineering, University of Queensland in 1996–99. He moved to NTU in 1999. He is an Editorial Board Member of the Evolutionary Computation Journal, MIT Press. He is an associate editor of the Applied Soft Computing (2018-), IEEE Trans on Cybernetics (2012 - ), IEEE Trans on Evolutionary Computation (2005 -), Information Sciences (Elsevier) (2009 - ), Pattern Recognition (Elsevier) (2001 - ) and Int. J. of Swarm Intelligence Research (2009 - ) Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation (2010 - ), an SCI Indexed Elsevier Journal. His co-authored SaDE paper (published in April 2009) won the "IEEE Trans. on Evolutionary Computation outstanding paper award" in 2012. His former PhD student, Dr Jane Jing Liang, won the IEEE CIS Outstanding PhD dissertation award, in 2014. His research interests include swarm and evolutionary algorithms, pattern recognition, forecasting, randomized neural networks, deep learning and applications of swarm, evolutionary & machine learning algorithms. His publications have been well cited (Googlescholar Citations: ~32k). His SCI indexed publications attracted over 1000 SCI citations in each calendar years 2013, 2014, 2015, 2016, 2017 and 2018. He was selected as one of the highly cited researchers by Thomson Reuters in 2015, 2016, 2017 and 2018 in computer science. He served as the General Chair of the IEEE SSCI 2013. He is an IEEE CIS distinguished lecturer (2018-2020). He has been a member of the IEEE (S'90, M'92, SM'00, Fellow’15) since 1990 and an elected AdCom member of the IEEE Computational Intelligence Society (CIS) in 2014-2016.
Noor H. Awad received the B.A and M.A degrees in computer engineering from Jordan University of Science and Technology, Irbid, Jordan, in 2011 and 2014, respectively. She submitted her Ph.D. thesis to the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
She is currently a postdoctoral research fellow at the department of computer science, University of Freiburg, Germany.
Her current research interests include evolutionary computation and its application in numerical optimization and real life optimization problems, optimization algorithms, differential evolution, and machine learning.
Mostafa Z. Ali received the Bachelor degree in Applied Mathematics at Jordan University of Science &Technology (JUST), Irbid, Jordan, in 2000. He finished his Masters in Computer Science at the University of Michigan-Dearborn, Michigan, USA in 2003. He finished his Ph.D. in computer science/Artificial Intelligence at Wayne State University, Michigan, USA in 2008.
He is an associate professor at the department of computer information systems at Jordan University of Science & Technology, Irbid, Jordan. His research interests include evolutionary computation, Cultural Algorithms, Virtual Reality, data mining, and Bioinformatics Databases. Dr. Ali is a member of the IEEE, the IEEE computer society, the American Association of Artificial Intelligence (AAAI), and the ACM.
Kalyanmoy Deb is Koenig Endowed Chair Professor at the Department of Electrical and Computer Engineering in Michigan State University (MSU), East Lansing, USA. Prof. Deb's main research interests are in genetic and evolutionary optimization algorithms and their application in optimization, modeling, and machine learning. He is largely known for his seminal research in developing and applying Evolutionary Multi-Criterion Optimization. Prof. Deb was awarded the prestigious `Infosys Prize' in 2012, `TWAS Prize' in Engineering Sciences in 2012, `CajAstur Mamdani Prize' in 2011, `JC Bose National Fellowship' in 2011, `Distinguished Alumni Award' from IIT Kharagpur in 2011, 'Edgeworth-Pareto' award in 2008, Shanti Swarup Bhatnagar Prize in Engineering Sciences in 2005, `Thomson Citation Laureate Award' from Thompson Reuters. His 2002 IEEE-TEC NSGA-II paper is judged as the Most Highly Cited paper and a Current Classic by Thomson Reuters having more than 4,200+ citations. He is a fellow of IEEE and International Society of Genetic and Evolutionary Computation (ISGEC). He has written two text books on optimization and more than 350+ international journal and conference research papers with Google Scholar citations of 55,000+ with h-index of 77. He is in the editorial board on 20 major international journals.
AI Competition for the Legends of the Three Kingdoms Game (三国杀)
LTK (Legends of the Three Kingdoms) is a popular board game worldwide (1). Its online version was first developed in 2008 (2). Since then, there have been many versions, including PC, Mac, Android, iOS. There exceeds 64.9 million downloads for mobile versions. In previous competitions, including Simulated Car Racing Championship (GECCO 2015), MicroRTS AI Competition (GECCO 2017), World Computer Chess Championships (IJCAI 2018), the relationships of competition or cooperation between the players are deterministic. The LTK game is a multi-agent, incomplete information game. Of particular interest is that players in LTK should learn the dynamics of competition and/or cooperation. This is a key feature we think for AI in multi-agent systems. In this competition, we offer a programming-language-free competition system, where agent receive and send data according to the JSON format. Thus, competition teams can develop AI algorithms via any programming language with a JSON parser support.
The competition platform will be finished in late 2018/early 2019.
AI algorithms (3) are expected to handle the dynamics of competition and/or cooperation in this competition. Hierarchical game with a macro-strategy may be a promising solution.
(1) https://en.wikipedia.org/wiki/Legends_of_the_Three_Kingdoms, 2018.
(2) Online version of LTK, http://web.sanguosha.com, 2018.
(3) Yannakakis, Georgios N and Togelius, Julian. Artificial Intelligence and Games. Springer, 2018.
Submission deadline for 2-page paper: April 3
Review: April 13
Notification: April 17
Camera-ready: April 24
Submission deadline for AI algorithms: June 1
Submission deadline:April 03, 2019
Xingguo Chen received the B.A. degree and PhD. degree in the Dept of Computer Science and Technology from Nanjing University, China in 2007, and 2013, respectively. He serves now as a lecturer and master's supervisor in the School of Computer Science & Technology, School of Software in Nanjing University of Posts and Telecommunications, China. His research interests include reinforcement learning, machine learning, and Game AI. He is now the learder of Game AI Research Group @NJUPT.
Duofeng Wu received the B.A. degree in the Dept of Computer Science and Technology from Nanjing University of Posts and Telecommunications, China in 2018. He is currently pursing for the master's degree. His research interests include multi-agent reinforcement learning and Game AI.
Binghui Xie is now a sophomore in computer science and technology at Nanjing University of Posts and Telecommunications. His interests include game design and front-end development.
Bi-Objective Optimisation for the Travelling Thief Problem
Real-world optimization problems often consist of several NP-hard combinatorial optimization problems that interact with each other. Such multi-component optimization problems are difficult to solve not only because of the contained hard optimization problems, but in particular, because of the interdependencies between the different components. Interdependence complicates a decision making by forcing each sub-problem to influence the quality and feasibility of solutions of the other sub-problems. This influence might be even stronger when one sub-problem changes the data used by another one through a solution construction process. Examples of multi-component problems are vehicle routing problems under loading constraints, the maximizing material utilization while respecting a production schedule, and the relocation of containers in a port while minimizing idle times of ships.
The goal of this competition is to provide a platform for researchers in computational intelligence working on multi-component optimization problems. The main focus of this competition is on the combination of TSP and Knapsack problems. However, we plan to extend this competition format to more complex combinations of problems (that have typically been dealt with individually in the past decades) in the upcoming years.
Preliminary pages, until the final ones go up:
Problem Description: https://www.egr.msu.edu/coinlab/blankjul/emo19-thief/
Markus Wagner is a Senior Lecturer at the School of Computer Science, University of Adelaide, Australia. He has done his PhD studies at the Max Planck Institute for Informatics in Saarbruecken, Germany and at the University of Adelaide, Australia. For the outcomes of his studies, he has received the university's Doctoral Research Medal - the first for this school.
His research topics range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. So far, he has been a program committee member 30 times, and he has written over 70 articles with over 70 different co-authors. He has contributed to GECCO as Workshop Chair in 2016 and 2017. He has chaired several education-related committees within the IEEE CIS, was Co-Chair of ACALCI 2017 and General Chair of ACALCI 2018.
Black Box Optimization Competition (BBComp)
The Black Box Optimization Competition is the first competition platform in the continuous domain where test problems are truly black boxes to participants. The only information known to optimizer and participant is the dimension of the problem, bounds on all variables, and a budget of black box queries. The competition covers single- and multi-objective optimization.
We will use the exact same format as last year. We have five competition tracks. They will open on January first and close on June 30, midnight, UTC. (updated)
Submission deadline:June 30, 2019
Tobias Glasmachers is a professor at Ruhr-University Bochum, Germany. He received his diploma (2004) and his doctorate degree (2008) from the Mathematics department of the Ruhr-University. Then he joined the swiss AI lab IDSIA for two years as a post-doc and then returned to Bochum in 2012. His research is located in the areas of machine learning and single- and multi-objective optimization in continuous spaces, in particular with evolution strategies.
Competition on Niching Methods for Multimodal Optimization
The aim of the competition is to provide a common platform that encourages fair and easy comparisons across different niching algorithms. The competition allows participants to run their own niching algorithms on 20 benchmark multimodal functions with different characteristics and levels of difficulty. Researchers are welcome to evaluate their niching algorithms using this benchmark suite, and report the results by submitting a paper to the main tracks of GECCO 2018 (i.e., submitting via the online submission system of GECCO 2018).
Submission deadline:April 03, 2019
Mike Preuss is Research Associate at ERCIS, University of Muenster, Germany. Previously, he was with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization. He is also active in computational intelligence methods for computer games, especially in procedural content generation (PGC) and realtime strategy games (RTS).
Michael G. Epitropakis received his B.S., M.S., and Ph.D. degrees from the Department of Mathematics, University of Patras, Patras, Greece. Currently, he is a Lecturer in Foundations of Data Science at the Data Science Institute and the Department of Management Science, in Lancaster University, Lancaster, UK. His current research interests include operations research, computational intelligence, evolutionary computation, swarm intelligence, machine learning and search-based software engineering. He has published more than 30 journal and conference papers. He is an active researcher on Multimodal Optimization, co-organizing series of workshops, special sessions, and competitions on Niching Methods for Multimodal Optimization in top tier Evolutionary Computation conferences. He is one of the founders and currently chairing the IEEE CIS Task Force on Multi-modal Optimization. He is an active member of the IEEE Computational Intelligence Society and the ACM SIGEVO.
Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is a full professor at the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks, complex systems, multiobjective optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a Vice-chair of IEEE CIS Task Force of Multi-Modal Optimization, and a former Chair of IEEE CIS Task Force on Large Scale Global Optimization. He was the General Chair of SEAL'08, a Program Co-Chair AI'09, and a Program Co-Chair for IEEE CEC’2012. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award".
Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is Professor in Computer Science at the University of Pretoria, and is appointed as the Director of the Institute for Big Data and Data Science. He holds the position of South African Research Chair in Artificial Intelligence, and leads the Computational Intelligence Research Group. His research interests include swarm intelligence, evolutionary computation, neural networks, artificial immune systems, and the application of these paradigms to data mining, games, bioinformatics, finance, and difficult optimization problems. He has published over 350 papers in these fields and is author of two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.
Evolutionary Computation in Uncertain Environments: A Smart Grid Application
Following the success of the previous edition at WCCI 2018 (http://www.gecad.isep.ipp.pt/WCCI2018-SG- COMPETITION/) we are relaunching this competition at major conferences in the field of computational intelligence. This GECCO 2019 competition proposes the optimization of a centralized day-ahead energy resource management problem in smart grids under environments with uncertainty. This year we increased the difficulty by proving a more challenging case study, namely with higher degree of uncertainty.
The GECCO 2019 competition on “Evolutionary Computation in Uncertain Environments: A Smart Grid Application” has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to an energy domain problem, namely the energy resource management problem under uncertain environments. The competition provides a coherent framework where participants and practitioners of CI can test their algorithms to solve a real-world optimization problem in the energy domain with uncertainty consideration, which makes the problem more challenging and worth to explore.
Submission deadline:April 30, 2019
Fernando Lezama received an M.Sc. degree (with Honors) in Electronic Engineering (2011), and a Ph.D. in ITCs (2014) both from the Monterrey Institute of Technology and Higher Education (ITESM), Mexico. Currently, he is a researcher at GECAD, Polytechnic of Porto, Portugal, where he works in the development of intelligent systems for optimization in smart grids. His research interests include computational intelligence, evolutionary computation, and optimization of smart grids and optical networks.
João Soares has a BSc in computer science and a master in Electrical Engineering in Portugal, namely Polytechnic of Porto. He attained his PhD degree in Electrical and Computer Engineering at UTAD university. He his a researcher at GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development. His research interests include optimization in power and energy systems, including heuristic, hybrid and classical optimization.
Zita Vale is full professor at the Polytechnic Institute of Porto and the director of the Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD). She received her diploma in Electrical Engineering in 1986 and her PhD in 1993, both from University of Porto. Zita Vale works in the area of Power and Energy Systems, with special interest in the application of Artificial Intelligence techniques. She has been involved in more than 50 funded projects related to the development and use of Knowledge-Based systems, Multi-Agent systems, Genetic Algorithms, Neural networks, Particle Swarm Intelligence, Constraint Logic Programming and Data Mining.
She published over 800 works, including more than 100 papers in international scientific journals, and more than 500 papers in international scientific conferences.
Internet of Things: Online Event Detection for Drinking Water Quality Control
For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.
Additionally, we are able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!
Competition Opens: End of January/Start of February 2019
Short Paper due: 03 April 2019
Final Submission: 30 June 2019
Submission deadline:June 30, 2019
Frederik is a Ph.D. student in the SPOTSeven Lab at CUAS (Cologne University of Applied Sciences). After earning his master's degree in Automation & IT at CUAS, he started working in the research project OWOS (Open Water Open Source). The project concentrates on improving and ensuring drinking water quality by online monitoring and event detection.
Steffen Moritz is a Research Associate at Cologne University of Applied Sciences. Before joining CUAS in 2016 he was working three years in R&D at Bosch Thermotechnology. Steffen earned his master’s degree in business information systems at TH Mittelhessen in Gießen. Steffen's research focuses on the application of ML algorithms in industrial sensor environments. He is also very active in the fields of Time Series Analysis and Imputation / Missing Data Treatment.
- Academic Background: Ph.D. (Dr. rer. nat.), TU Dortmund University, 2005, Computer Science.
- Professional Experience: Shareholder, Bartz & Bartz GmbH, Germany, 2014 – Present; Speaker, Research Center Computational Intelligence plus, Germany, 2012 – Present; Professor, Applied Mathematics, TH Köln, Germany, 2006 – Present.
- Professional Interest: Computational Intelligence; Simulation; Optimization; Statistical Analysis; Applied Mathematics.
- ACM Activities: Organizer of the GECCO Industrial Challenge, SIGEVO, 2011 – Present; Event Chair, Evolutionary Computation in Practice Track, SIGEVO, 2008 – Present; Tutorials Evolutionary Computation in Practice, SIGEVO, 2005 – 2013; GECCO Program Committee Member, Session Chair, SIGEVO, 2004 – Present.
- Membership and Offices in Related Organizations: Program Chair, International Conference Parallel Problem Solving from Nature, Jozef Stefan Institute, Slovenia, 2014; Program Chair, International Workshop on Hybrid Metaheuristics, TU Dortmund University, 2006; Member, Special Interest Group Computational Intelligence, VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik, 2008 – Present.
- Awards Received: Innovation Partner, State of North Rhine-Westphalia, Germany, 2013; One of the top 20 researchers in applied science by the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia, 2017.
Virtual Creatures Competition
The Virtual Creatures Competition showcases evolution's ability to craft interesting virtual creatures with well-adapted morphologies and controllers.
The purpose of the contest is not to build a system to solve a task better than any other system; rather, the goal is to engage our imagination and creativity---to expand our conception of life and intelligence, and how these things might materialize.
Video entries demonstrating evolved virtual creatures will be judged by technical achievement, aesthetic appeal, innovation, and perceptual animacy (perceived aliveness).
Submission deadline:June 23, 2019
Sam Kriegman is a PhD student at the University of Vermont, advised by Josh Bongard. Sam's research uses robots as scientific tools to answer questions about biology and cognitive science, though its principal raison d'être is an external social need: useful, safe, autonomous and adaptive machines.
Nick Cheney is a Research Assistant Professor of Computer Science at the University of Vermont, where he directs the UV M Neurobotics Lab. He earned a PhD in Computational Biology from Cornell University, where he was advised by Hod Lipson and Steven Strogatz. Nick has held many visiting and affiliated researcher positions, including with the Vermont Complex Systems Center, Columbia Creative Machines Lab, Santa Fe Institute, and NASA Ames Research Center. Nick's research focuses on bio-inspired optimization of embodied artificial intelligence — working at the interface of evolutionary optimization, development, and reinforcement learning. Nick is also passionate about scientific visualization and communication, and videos of his research on evolved soft robots have won prizes at GECCO, AAAI, IJCAI, and ALIFE conferences. His work has been covered by popular media outlets, such as NBC, Popular Science, and Discover Magazine, and featured in a TEDx talk.
Sebastian Risi is an Associate Professor at the IT University of Copenhagen where he is part of the Center for Computer Games Research and the Robotics, Evolution and Art Lab (REAL). His interests include computational intelligence in games, neuroevolution, evolutionary robotics and human computation. Risi completed his PhD in computer science from the University of Central Florida. He has won several best paper awards at GECCO, EvoMusArt, IJCNN, and the Continual Learning Workshop at NIPS for his work on adaptive systems, the HyperNEAT algorithm for evolving complex artificial neural networks, and music generation.