Loading...
 

Competitions



GECCO 2020 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).


TitleOrganizers
Competition on Single Objective Bound Constrained Numerical Optimization
  • Ponnuthurai Nagaratnam Suganthan
  • Mostafa Ali
  • J. J. Liang
  • B. Y. Qu
  • Cai Tong Yue
  • Kenneth Price
Competition on Single Objective Constrained Numerical Optimization
  • Ponnuthurai Nagaratnam Suganthan
  • Guohua Wu
  • Mostafa Ali
  • Rammohan Mallipeddi
  • Abhishek Kumar
  • Swagatam Das
Competition on the optimal camera placement problem (OCP) and the unicost set covering problem (USCP).
  • Mathieu Brévilliers
  • Lhassane Idoumghar
  • Julien Kritter
  • Julien Lepagnot
Dota 2 1-on-1 Shadow Fiend Laning Competition
  • Robert Smith
  • Malcolm Heywood
Dynamic Stacking Optimization in Uncertain Environments
  • Andreas Beham
  • Stefan Wagner
  • Sebastian Raggl
Evolutionary Computation in the Energy Domain: Smart Grid Applications
  • Joao Soares
  • Fernando Lezama
  • Bruno Canizes
  • Zita Vale
Evolutionary Multi-task Optimization
  • Feng Liang
  • Kai Qin
  • Abhishek Gupta
  • Yuan Yuan
  • Eric O Scott
  • Yew Soon Ong
Game Benchmark Competition
  • Vanessa Volz
  • Tea Tušar
  • Boris Naujoks
GECCO 2020 Competition on Niching Methods for Multimodal Optimization
  • Mike Preuss
  • Michael Epitropakis
  • Xiaodong Li
Industrial Challenge
  • Frederik Rehbach
  • Thomas Bartz-Beielstein
Open Optimization Competition 2020
  • Carola Doerr
  • Olivier Teytaud
  • Jérémy Rapin
  • Thomas Baeck

Competition on Single Objective Bound Constrained Numerical Optimization

Description:

This competition challenges participants to optimize a test-bed consisting of 10 bound constrained functions that range from easy to hard. Each function is sampled at multiple dimensions to provide insights into algorithmic scaling performance. This year's competition extends the limits for the maximum allowed number of function evaluations beyond those chosen for prior competitions with the goal of learning if the additional time translates into significantly improved final function values.

Submission deadline:

Official webpage:

Organizers:

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 Singapore in 1999. He was an Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013-2018) and an associate editor of the IEEE Trans on Cybernetics (2012 - 2018). He is an associate editor of Applied Soft Computing (Elsevier, 2018-), Neurocomputing (Elsevier, 2018-), 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.

Mostafa Ali

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. He is an associate editor of the Swarm and Evolutionary Computation (SWEVO, an Elsevier journal). His research interests include evolutionary computation, Machine Learning, Deep Learning, Virtual/Augmented Reality, and data mining. Dr. Ali is a member of the IEEE, the IEEE computer society, the American Association of Artificial Intelligence (AAAI), and the ACM.

 

J. J. Liang

J.J. Liang received the B.E. degree from Harbin Institute of Technology, China and the Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. She is currently a Professor in the School of Electrical Engineering, Zhengzhou University, China. Her main research interests are evolutionary computation, swarm intelligence, multimodal optimization, multi-objective optimization and neural network.

 

B. Y. Qu

B.Y. Qu received the B.E. degree and Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He is currently a Professor in the School of Electric and Information Engineering, Zhongyuan University of Technology, China. His research interests include machine learning, neural network, genetic and evolutionary algorithms, swarm intelligence, and multi-objective optimization.

 

Cai Tong Yue

Caitong Yue received the B.E. degree in the School of Electrical Engineering from Zhengzhou University, China in 2014. He is pursuing his Ph.D. degree in the School of Electrical Engineering, Zhengzhou University, China. He studies in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore from June 2019 to June 2020.His research interests include sparse optimization, pattern recognition, neural network, particle swarm optimization, and multimodal multiobjective optimization.

Kenneth Price

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”. Ken has authored or coauthored 7 additional peer-reviewed papers, contributed chapters to three books on optimization and has served as a reviewer for 12 different journals. He is also the creator of the 100-Digit Challenge on Single Objective Real-Parameter Optimization competition.

  

Competition on Single Objective Constrained Numerical Optimization

Description:

The goals of this competition are to evaluate the current state of the art in single objective numerical optimization with general constraints and to propose novel benchmark problems with diverse characteristics. Under the above scenarios, for the first time a set of real-world constrained optimization benchmark will be released for this competition.

Submission deadline:

Official webpage:

Organizers:

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 Singapore in 1999. He was an Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013-2018) and an associate editor of the IEEE Trans on Cybernetics (2012 - 2018). He is an associate editor of Applied Soft Computing (Elsevier, 2018-), Neurocomputing (Elsevier, 2018-), 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.

 

Guohua Wu

Guohua Wu received the B.S. degree in Information Systems and Ph.D degree in Operations Research from National University of Defense Technology, China, in 2008 and 2014, respectively. During 2012 and 2014, he was a visiting Ph.D student at University of Alberta, Edmonton, Canada. He is now a Professor at the School of Traffic and Transportation Engineering, Central South University, Changsha, China.
His current research interests include scheduling, evolutionary computation and machine learning. He has authored more than 60 referred papers including those published in IEEE TCYB, IEEE TSMCA, INS, COR. He serves as an Associate Editor of Swarm and Evolutionary Computation, an editorial board member of International Journal of Bio-Inspired Computation, a Guest Editor of Information Sciences and Memetic Computing. He is a regular reviewer of more than 20 journals including IEEE TEVC, IEEE TCYB, IEEE TFS.

Mostafa Ali

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. He is an associate editor of the Swarm and Evolutionary Computation (SWEVO, an Elsevier journal). His research interests include evolutionary computation, Machine Learning, Deep Learning, Virtual/Augmented Reality, and data mining. Dr. Ali is a member of the IEEE, the IEEE computer society, the American Association of Artificial Intelligence (AAAI), and the ACM.

 

Rammohan Mallipeddi

Rammohan Mallipeddi is an Associate Professor working in the School of Electronics Engineering, Kyungpook National University (Daegu, South Korea). He received Master’s and PhD degrees in computer control and automation from the School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, in 2007 and 2010, respectively. His research interests include evolutionary computing, artificial intelligence, image processing, digital signal processing, robotics, and control engineering. He co-authored papers published IEEE TEVC, etc. Currently, he serves as an Associate Editor for “Swarm and Evolutionary Computation”, an international journal from Elsevier and a regular reviewer for journals including IEEE TEVC and IEEE TCYB.

 

Abhishek Kumar

Abhishek Kumar received the B.Tech degree in Electrical Engineering from Uttarakhand Technical University, Dehradun in 2013. He submitted his Ph.D. thesis to the Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi, India. He was awarded with “Young Researcher Award-2016” from IEEE CIS Chapter, UP section, IIT Kanpur. His co-authored work “EBOwithCMAR” won the IEEE CEC-2017 competition on bound-constrained optimization problem.

His current research interests include swarm and evolutionary computation and its application in real-world optimization problem especially in Power System Optimization applications, optimization algorithms, and machine learning.

 

Swagatam Das

Swagatam Das received the B. E. Tel. E., M. E. Tel. E (Control Engineering specialization) and Ph. D. degrees, all from Jadavpur University, India, in 2003, 2005, and 2009 respectively. Swagatam Das is currently serving as an associate professor at the Electronics and Communication Sciences Unit of the Indian Statistical Institute, Kolkata, India. His research interests include evolutionary computing, deep learning and non convex optimization in general. Dr. Das has published more than 300 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, Pattern Recognition (Elsevier),Neurocomputing (Elsevier),Engineering Applications of Artificial Intelligence (Elsevier), and Information Sciences (Elsevier). He is a founding Section Editor of Springer Nature Computer Science journal since 2019. Dr. Das has 18000+ Google Scholar citations and an H-index of 63 till date. He has been associated with the international program committees of several regular international conferences including IEEE CEC, IEEE SSCI, SEAL, GECCO, AAAI, and SEMCCO. He has acted as guest editors for special issues in journals like IEEE Transactions on Evolutionary Computation 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.

  

Competition on the optimal camera placement problem (OCP) and the unicost set covering problem (USCP).

Description:

The use of camera networks is now common to perform various surveillance tasks. These networks can be implemented together with intelligent systems that analyze video footage, for instance, to detect events of interest, or to identify and track objects or persons. According to (6), whatever the operational needs are, the quality of service depends on the way in which the cameras are deployed in the area to be monitored (in terms of position and orientation angles). Moreover, due to the prohibitive cost of setting or modifying such a camera network, it is required to provide a priori a configuration that minimizes the number of cameras in addition to meeting the operational needs. In this context, the optimal camera placement problem (OCP) is of critical importance, and can be generically formulated as follows. Given various constraints, usually related to coverage or image quality, and an objective to optimise (typically, the cost), how can the set of positions and orientations which best (optimally) meets the requirements be determined?
More specifically, in this competition, the objective will be to determine camera locations and orientations which ensure complete coverage of the area while minimizing the cost of the infrastructure. To this aim, a discrete approach is considered here : the surveillance area is reduced to a set of three-dimensional sample points to be covered, and camera configurations are sampled into so-called candidates each with a given set of position and orientation coordinates. A candidate can have several samples within range, and a sample can be seen by several candidates. Now, the OCP comes down to select the smallest subset of candidates which covers all the samples.
According to (5), the OCP is structurally identical to the unicost set covering problem (USCP), which is one of Karp's well-known NP-hard problems (3). The USCP can be stated as follows: given a set of elements I (rows) to be covered, and a collection of sets J (columns) such that the union of all sets in J is I, find the smallest subset C of J such that the union of all sets in C is I. In other words, identify the smallest subset of J wich covers I. As pointed out in (5), many papers dealing with the OCP use this relationship implicitly, but few works done on the USCP have been applied or adapted to the OCP, and vice versa. In very recent years however, approaches from the USCP literature have been successfully applied in the OCP context on both academic (7,8) and real-world (4) problem instances. These works suggest that bridges can be built between these two bodies of literature to improve the results obtained so far on both USCP and OCP problems.
Firstly, the main goal of this competition is to encourage innovative research works in this direction, by proposing to solve OCP problem instances stated as USCP. Secondly, to this day, no benchmark has been established for the OCP, which makes difficult to provide a fair comparison of all various propositions from the OCP literature (5): this competition is thus an opportunity to propose a benchmark testbed for the OCP. Thirdly, this competition is a way of attracting the interest of the scientific community in new challenging USCP problem instances, given that, to the best of our knowledge, the last challenge on set covering problems was a competition called FASTER (Ferrovie Airo Set covering TendER), jointly organized by the Italian railway company (Ferrovie dello Stato SpA)and the Italian Operational Research Society (AIRO) in 1994 (2), and whose problem instances are now part of Beasley's standard OR library (1).

(1) J. E. Beasley. Or-library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41(11):1069–1072, 1990.
(2) Alberto Caprara, Matteo Fischetti, and Paolo Toth. A heuristic method for the set covering problem. Operations Research, 47(5):730–743, 1999.
(3) Richard M. Karp. Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA, 1972.
(4) J. Kritter, M. Brévilliers, J. Lepagnot, and L. Idoumghar. On the real-world applicability of state-of-the-art algorithms for the optimal camera placement problem. In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pages 1103–1108, April 2019.
(5) Julien Kritter, Mathieu Brévilliers, Julien Lepagnot, and Lhassane Idoumghar. On the optimal placement of cameras for surveillance and the underlying set cover problem. Applied Soft Computing, 74:133 – 153, 2019.
(6) Junbin Liu, Sridha Sridharan, and Clinton Fookes. Recent advances in camera planning for large area surveillance: A comprehensive review. ACM Comput. Surv., 49(1):6:1–6:37, May 2016.
(7) Brévilliers M., Lepagnot J., Kritter J., and Idoumghar L. Parallel preprocessing for the optimal camera placement problem. International Journal of Modeling and Optimization, 8(1):33 – 40, 2018.
(8) Brévilliers M., Lepagnot J., Idoumghar L., Rebai M., and Kritter J. Hybrid differential evolution algorithms for the optimal camera placement problem. Journal of Systems and Information Technology, 20(4):446 – 467, 2018.

IMPORTANT DATES :
March 20, 2020: Deadline to register for this competition, to be allowed to submit an abstract for publication in the GECCO Companion
April 3, 2020: Submission deadline for the GECCO Companion abstracts and the corresponding solution files
April 17, 2020: Notification of acceptance for GECCO Companion abstracts
April 24, 2020: Deadline to submit camera-ready abstracts for the GECCO Companion
May 22, 2020: General deadline to register for this competition
June 5, 2020: End of the competition, and submission deadline for: solution files, algorithm description, and experimental setting
July 8-12, 2020: GECCO 2020 Conference, and announcement of the competition results

Submission deadline:

June 05, 2020

Official webpage:

http://www.mage.fst.uha.fr/brevilliers/gecco-2020-ocp-uscp-competition/

Organizers:

 

Mathieu Brévilliers

Mathieu Brévilliers received in 2008 his PhD degree in computer science from the University of Haute-Alsace (UHA), Mulhouse, France. He spent one year at the Grenoble Intitute of Technology (Grenoble INP, France) as temporary lecturer and researcher, and then has been hired by the UHA in 2009 as Associate Professor. Since 2014, he is part of the optimization team of the IRIMAS research institute. His main research interests include hybrid metaheuristics and their applications, massively parallel and distributed algorithms, and machine learning techniques.

 

Lhassane Idoumghar

Lhassane Idoumghar received in 2012 his accreditation to supervise research from University of Haute-Alsace, Mulhouse, France. Since 2015, he is Full Professor with University of Haute-Alsace and he is now head of the IRIMAS research institute in computer science, mathematics, automation and signal. His research activities include dynamic optimization, single/multiobjective optimization, uncertain optimization by hybrid metaheuristics, distributed and massively parallel algorithms.

 

Julien Kritter

Julien Kritter obtained both his BSc. (2015) and his MSc. (2017) in computer science and applied mathematics from the University of Le Havre, France. His research interests include several branches of artificial intelligence and operational research, in which he is now working towards a PhD. His work in the optimisation research team at the IRIMAS institute revolves around the design of efficient methods for large-scale, real-world optimisation problems.

 

Julien Lepagnot

Julien Lepagnot received his PhD in computer science in 2011 from University Paris 12, France. Since 2012, he is an associate professor in computer science at University of Haute-Alsace, France, in which he belongs to the OMEGA team of the IRIMAS research institute in computer science, mathematics, automation and signal. His main research interests include hybrid metaheuristics and their applications, machine learning and dynamic optimization.

  

Dota 2 1-on-1 Shadow Fiend Laning Competition

Description:

The Dota 2 game represents an example of a multiplayer online battle arena video game. The underlying goal of the game is to control the behaviour/strategy for a ‘hero’ character. Each hero possesses unique abilities, thus resulting in different performance tradeoffs. Moreover, the hero acts with a team of ‘creeps’ who have predefined behaviours, which can be influenced by the interaction between their hero and the opposing team. In short, the hero operates collaboratively with its own creeps and defensive structures (called towers) to defeat the opponent team (kill the opponent hero twice, or destroy their tower). In addition, there is an underlying economy in which developments in the game influence the amount of wealth received by each team. As a team’s wealth increases, then the hero’s abilities improve.

This competition will assume the 1-on-1 mid lane configuration of Dota 2 using the Shadow Fiend hero. Such a configuration still includes many of the properties that have turned the game into an ‘e-sport’, but without the computational overhead of solving the task for all heroes under multi-lane settings. Specific properties that make the 1-on-1 game challenging include: 1) the need to navigate a partially observable world under ego-centric sensor information, 2) state information that is high-dimensional, but subject to variation through the ‘fog-of-war’, 3) high-dimensional action space that is both discrete, continuous valued and context specific, 4) learning hero policies that act collectively with creeps, 5) supporting real-time decision making at frame-rate, and 6) the underlying physics of the game vary the times of day and introduce stochastic states.

Participants will create a Dota 2 Shadow Fiend hero agent based on a preset API provided by the organizers.

The competition entrants will be required to engage in a 1v1 match against the built-in Shadow Fiend hero AI, where the winner is determined by number of matches won. Evaluation will be performed against the top three levels of built-in hero over multiple games.

Submission deadline:

June 07, 2020

Official webpage:

https://web.cs.dal.ca/~dota2

Organizers:

 

Robert Smith

Robert Smith is a PhD candidate at the Faculty of Computer Science at Dalhousie University, Canada. He has published on the topic of competitive and co-operative coevolution of reinforcement learning agents for solving Rubic’s Cube configurations, and navigation under partially observable environments such as VizDoom and Dota 2. He is the ACM student chapter representative at Dalhousie University.

 

Malcolm Heywood

Malcolm Heywood is a Professor of Computer Science at Dalhousie University, Canada. He has a particular interest in scaling up the tasks that GP can potentially be applied to. His current research is attempting to coevolve behaviours capable of demonstrating general game AI and multi-task learning under video game environments. Dr. Heywood is a member of the editorial board for Genetic Programming and Evolvable Machines (Springer). He was a track co-chair for the GECCO GP track in 2014 and a co-chair for European Conference on Genetic Programming in 2015 and 2016.

  

Dynamic Stacking Optimization in Uncertain Environments

Description:

Stacking problems are central to multiple billion-dollar industries. The container shipping industry needs to stack millions of containers every year. In the steel industry the stacking of steel slabs, blooms, and coils needs to be carried out efficiently, affecting the quality of the final product. The immediate availability of data – thanks to the continuing digitalization of industrial production processes – makes the optimization of stacking problems in highly dynamic environments feasible.

In this competition, a dynamic environment is provided that represents a simplified stacking scenario. Blocks arrive continuously at a fixed arrival location from which they have to be removed swiftly. If the arrival location is full, the arrival of additional blocks is not possible. To avoid such a state, there is a range of buffer stacks that may be used to store blocks. Each block has a due date before which it should be delivered to the customer. However, blocks may leave the system only when they become ready, i.e., some time after their arrival. To deliver a block it must be put on the handover stack – which must contain only a single block at any given time. Once a block is put on the handover, some time is required for delivery during which the handover is not available. There is a single crane that may move blocks from arrival to buffer, between buffers, and from buffer to handover. The optimization must control this crane in that it reacts to changes with a sequence of moves that are to be carried out. The control does not have all information about the world. It may know on the release time of the next couple of blocks, but not further. It does not know when blocks become ready, the time required to move a certain block, the time a delivery requires. However, these are random variables and may thus be learnt through observation. The control will be measured along several performance indicators – from most to least important: Total number of blocks delivered on time, total number of blocks delivered, and number of crane manipulations. In addition the number of erroneous moves suggested will be tracked.

The dynamic environment is implemented in form of a realtime simulation which provides the necessary change events. The simulation runs in a separate process and publishes its world state and change events via message queuing (ZeroMQ), and also listens for crane orders. Thus, control algorithms may be implemented as standalone applications using a wide range of programming languages. Exchanged messages are encoded using protocol buffers – again libraries are available for a large range of programming languages. Participants are expected to provide a docker image in which the control algorithm runs based on either windows server or ubuntu linux images.

Submission deadline:

Official webpage:

Organizers:

 

Andreas Beham

Andreas Beham received his MSc in computer science in 2007 and his PhD in engineering sciences in 2019, both from Johannes Kepler University Linz, Austria. He works as senior researcher at the R&D facility at University of Applied Sciences Upper Austria, Hagenberg Campus and is leading several funded research projects. Dr. Beham is co-architect of the open source software environment HeuristicLab and member of the Heuristic and Evolutionary Algorithms Laboratory (HEAL) research group led by Dr. Affenzeller. He has published more than 80 documents indexed by SCOPUS and applied evolutionary algorithms, metaheuristics, mathematical optimization, data analysis, and simulation-based optimization in industrial research projects. His research interests include applying dynamic optimization problems, algorithm selection, and simulation-based optimization and innovization approaches in practical relevant projects.

Stefan Wagner

Stefan Wagner received his MSc in computer science in 2004 and his PhD in technical sciences in 2009, both from Johannes Kepler University Linz, Austria. From 2005 to 2009 he worked as associate professor for software project engineering and since 2009 as full professor for complex software systems at the Campus Hagenberg of the University of Applied Sciences Upper Austria. From 2011 to 2018 he was also CEO of the FH OÖ IT GmbH, which is the IT service provider of the University of Applied Sciences Upper Austria. Dr. Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) and is project manager and head architect of the open-source optimization environment HeuristicLab. He works as project manager and key researcher in several R&D projects on production and logistics optimization and his research interests are in the area of combinatorial optimization, evolutionary algorithms, computational intelligence, and parallel and distributed computing.

 

Sebastian Raggl

Sebastian Raggl received his MSc in bioinformatics in 2014 from the University of Applied Sciences Upper Austria. He is currently pursuing his PhD at the Johannes Kepler University Linz, Austria. Since 2015 he is a member of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) where he is working on several industrial research projects. He has focused on stacking problems in the steel industry for which he has acquired a lot of experience in the application domain, but also in the scientific state of the art.

  

Evolutionary Computation in the Energy Domain: Smart Grid Applications

Description:

Following the success of the previous editions at WCCI 2018 and CEC 2019 we are launching a more challenging competition at major conferences in the field of computational intelligence. This WCCI 2020 competition proposes two test beds in the energy domain:
Testbed 1) optimization of a centralized day-ahead energy resource management problem in smart grids under environments with uncertainty. This test bed is similar to the past challenge using a challenging 500-scenario case study with high degree of uncertainty. We also add some restrictions to the initialization of initial solution and the allowed repairs and tweak-heuristics.
Testbed 2) bi-level optimization of end-users’ bidding strategies in local energy markets (LM). This test bed is constructed under the same framework of the past competitions (therefore, former competitors can adapt their algorithms to this new testbed) , representing a complex bi-level problem in which competitive agents in the upper-level try to maximize their profits, modifying and depending on the price determined in the lower-level problem (i.e., the clearing price in the LM), thus resulting in a strong interdependence of their decisions.

Note: Both testbeds are developed to run under the same framework of past competitions.

Competition goals:

The WCCI & GECCO 2020 competition on “Evolutionary Computation in the Energy Domain: Smart Grid Applications” has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to energy domain problems, namely the energy resource management problem under uncertain environments and the optimal bidding of energy aggregators in local markets. The competition provides a coherent framework where participants and practitioners of CI can test their algorithms to solve two real-world optimization problems in the energy domain. The participants have the opportunity to evaluate if their algorithms can rank well in both problems since we understand the validity of the “no-free lunch theorem”, making this contest a unique opportunity worth to explore the applicability of the developed approaches in real-world problems beyond the typical benchmark and standardized CI problems.

Rules:
-Participants will propose and implement metaheuristic algorithms (e.g., evolutionary algorithms, swarm intelligence, estimation of distribution algorithm, etc.) to solve two testbeds problems in the energy domain.
-The organizers provide a framework, implemented in MATALAB© 2014b 64 bits, in which participants can easily test their algorithms (we also provide a differential evolution algorithm implementation as an example). The guidelines include the necessary information to understand the problems, how the solutions are represented, and how the fitness function is evaluated. Those elements are common for all participants.
-Since the proposed algorithms might have distinct sizes of population and run for a variable number of iterations, a maximum number of “50000 function evaluations” is allowed in each trial for all participants. The convergence properties of the algorithms are not a criterion to be qualified in this competition.
-20 independent trials should be performed in the framework by each participant.

Submission deadline:

May 31, 2020

Official webpage:

http://www.gecad.isep.ipp.pt/ERM-Competitions

Organizers:

Joao Soares

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.

Fernando Lezama

Fernando Lezama received an M.Sc. degree (with Honors) in Electronic Engineering (2011), and a Ph.D. in ICTs (2014) both from the Monterrey Institute of Technology and Higher Education (ITESM), Mexico. Currently, he is a researcher at GECAD, 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.

 

Bruno Canizes

Bruno Canizes received the Ph.D. degree in Computer Engineering in the field of Smart Power Networks from the University of Salamanca (USAL) - Spain in 2019. Presently, he is a Researcher at GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development of ISEP/IPP. His research interests include distribution network operation and reconfiguration, smart grids, smart cities, electric mobility, distributed energy resources management, power systems reliability, future power systems, optimization, electricity markets and intelligent house management systems.

Zita Vale

Zita Vale (S’86–M’93–SM’10) received the diploma degree in electrical engineering and the Ph.D. degree, both from the University of Porto, Porto, Portugal, in 1986 and 1993, respectively. She is currently a Full Professor with the Polytechnic of Porto, Porto, Portugal. Her main research interests include artificial intelligence applications to power system operation and control, electricity markets, and distributed generation.

  

Evolutionary Multi-task Optimization

Description:

The original inspiration of artificial intelligence (AI) was to build autonomous systems that were capable of demonstrating human-like behaviours within certain application areas. However, given the present-day data deluge, rapid increase in computational resources, and key improvements in machine learning algorithms, modern AI systems have begun to far exceed humanly achievable performance across a variety of domains. Some well known examples of this reality include IBM Watson winning Jeopardy!, and Google DeepMind’s AlphaGo beating the world’s leading Go player. Given such advances, it is deemed that what we foresee for AI in the future need no longer be limited to an anthropomorphic vision. Indeed, it may be more meaningful to build AI systems that complement and augment human intelligence, excelling at those tasks for which humans are ill-equipped. In this regard, one of the long-standing goals of AI has been to effectively multitask; i.e., learning to solve many tasks at the same time. It is worth noting that although humans are generally unable to tackle multiple problems simultaneously, or within short timespans – as interleaving more than one task usually entails a considerable switching cost during which the brain must readjust from one to the other – machines are largely free from such computational bottlenecks. Thus, not only can machines move more fluidly between tasks, but, when related tasks are bundled together, it is also possible for them to seamlessly transfer data (encapsulating some problem-solving knowledge) among them. As a result, while an AI attempts to solve a complex task, several other simpler ones may be unintentionally solved. Moreover, the knowledge learned unintentionally can then be harnessed for intentional use.

In line with the above, evolutionary multitasking is an emerging concept in computational intelligence that realises the theme of efficient multi-task problem-solving in the domain of numerical optimization. It is worth noting that in the natural world, the process of evolution has, in a single run, successfully produced diverse living organisms that are skilled at survival in a variety of ecological niches. In other words, the process of evolution can itself be thought of as a massive multi-task engine where each niche forms a task in an otherwise complex multifaceted fitness landscape, and the population of all living organisms is simultaneously evolving to survive in one niche or the other. Interestingly, it may happen that the genetic material evolved for one task is effective for another as well, in which case the scope for inter-task genetic transfers facilitates frequent leaps in the evolutionary progression towards superior individuals. Being nature-inspired optimisation procedures, it has recently been shown that evolutionary algorithms (EAs) are not only equipped to mimic Darwinian principles of “survival-of-the-fittest”, but their reproduction operators are also capable of inducing the afore-stated inter-task genetic transfers in multitask optimisation settings; although, the practical implications of the latter are yet to be fully studied and exploited in the literature. The potential efficacy of this new perspective, as opposed to traditional approaches of solving each optimisation problem in isolation, has nevertheless been unveiled by so-called multi-factorial EAs (MFEAs).

Evolutionary multitasking opens up new horizons for researchers in the field of evolutionary computation. It provides a promising means to deal with the ever-increasing number, variety and complexity of optimisation tasks. More importantly, rapid advances in cloud computing could eventually turn optimisation into an on-demand service hosted on the cloud. In such a case, a variety of optimisation tasks would be simultaneously executed by the service engine where evolutionary multitasking may harness the underlying synergy between multiple tasks to provide consumers with faster and better solutions.

Submission deadline:

Official webpage:

Organizers:

 

Feng Liang

Liang Feng received the PhD degree from the School of Computer Engineering, Nanyang Technological University, Singapore, in 2014. He was a Postdoctoral Research Fellow at the Computational Intelligence Graduate Lab, Nanyang Technological University, Singapore. He is currently an Assistant Professor at the College of Computer Science, Chongqing University, China. His research interests include Computational and Artificial Intelligence, Memetic Computing, Big Data Optimization and Learning, as well as Transfer Learning. He is serving as the Chair of the IEEE Task Force on “Transfer Learning and Transfer Optimization”, and also the PC member of the IEEE Task Force on “Memetic Computing”. He had co-organized and chaired the Special Session on “Memetic Computing” held at CEC’16, CEC’17, CEC’18, CEC’19, and the Special Session on ""Transfer Learning in Evolutionary Computation"" held at CEC’18, CEC’19.

 

Kai Qin

Kai Qin is an associate professor of Department of Computer Science and Software Engineering Swinburne University of Technology, Australia. He received the PhD degree at Nanyang Technology University (Singapore) in 2007. From 2007 to 2009, he worked as a Postdoctoral Fellow at the University of Waterloo (Waterloo, Canada). From 2010 to 2012, he worked at INRIA (Grenoble, France), first as a Postdoctoral Researcher and then as an Expert Engineer. He joined RMIT University in 2012 as a Vice-Chancellor’s Research Fellow, and then worked as a Lecturer between 2013 and 2016 and a Senior Lecturer from 2017. His major research interests include evolutionary computation, machine learning, computer vision, GPU computing and services computing. Two of his authored/coauthored journal papers have become the 1st and 4th most-cited papers among all of the papers published in the IEEE Transactions on Evolutionary Computation (TEVC) over the last 10 years according to the Web of Science Essential Science Indicators. He is the recipient of the 2012 IEEE TEVC Outstanding Paper Award. One of his conference papers was nominated for the best paper at the 2012 Genetic and Evolutionary Computation Conference (GECCO’12). He won the Overall Best Paper Award at the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES’14). He is serving as the Chair of the IEEE Emergent Technologies Task Force on “Collaborative Learning and Optimization”, promoting the emerging research of the synergy between machine learning and optimization. He had coorganized and chaired the Special Session on “Differential Evolution: Past, Present and Future” held at CEC’12, CEC’13, CEC’14, CEC’15, CEC’16 and CEC’17.

 

Abhishek Gupta

Abhishek Gupta received his PhD in Engineering Science from the University of Auckland, New Zealand, in the year 2014. He graduated with a Bachelor of Technology degree in the year 2010, from the National Institute of Technology Rourkela, India. He currently serves as a Research Scientist in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He has diverse research experience in the field of computational science, ranging from the numerical modelling of solids and fluids, to topics in computational intelligence. His recent research interests are in the development of memetic computing as a tool for automated knowledge extraction and transfer across problems in evolutionary design.

 

Yuan Yuan

Yuan Yuan is a Postdoctoral Fellow in the Department of Computer Science and Engineering, Michigan State University, USA. He received the PhD degree with the Department of Computer Science and Technology, Tsinghua University, China, in July 2015. From January 2014 to January 2015 he was a visiting PhD student with the Centre of Excellence for Research in Computational Intelligence and Applications, University of Birmingham, UK. He worked as a Research Fellow at the School of Computer Science and Engineering, Nangyang Technological University, Singapore, from October 2015 to November 2016. His current research interests include multi-objective optimization, genetic improvement, and evolutionary multitasking. Two of his conference papers were nominated for the best paper at the GECCO 2014 and GECCO 2015, respectively.

 

Eric O Scott

Eric Scott is a PhD candidate at George Mason University and a Senior Artificial Intelligence Engineer at MITRE Corporation in Northern Virginia. His research focuses on heuristic optimization algorithms and their applications to simulation and modeling in a variety of fields. He holds a double B.Sc. in Computer Science and Mathematics from Andrews University in Berrien Springs, Michigan, and a M.Sc. in Computer Science from George Mason University.

Yew Soon Ong

Yew-Soon Ong is Professor and Chair of the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems and Programme Principal Investigator of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab. He was Director of the Centre for Computational Intelligence or Computational Intelligence Laboratory from 2008-2015. He received his Bachelors and Masters degrees in Electrical and Electronics Engineering from Nanyang Technological University and subsequently his PhD from University of Southampton, UK. He is founding Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence, founding Technical Editor-In-Chief of Memetic Computing Journal (Springer), Associate Editor of IEEE Computational Intelligence Magazine, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Network & Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, International Journal of Systems Science, Soft Computing Journal, and chief editor of Book Series on Studies in Adaptation, Learning, and Optimization as well as Proceedings in Adaptation, Learning, and Optimization He is also guest editors of IEEE Transactions on Evolutionary Computation, IEEE Trans SMC-B, Soft Computing Journal, Journal of Genetic Programming and Evolvable Machines, co-edited several books, including Multi-Objective Memetic Algorithms, Evolutionary Computation in Dynamic and Uncertain Environments, and a volume on Advances in Natural Computation published by Springer Verlag. He served as Chair of the IEEE Computational Intelligence Society Emergent Technology Technical Committee (ETTC) from 2011-2012, and has been founding chair of the Task Force on Memetic Computing in ETTC since 2006 as well as a member of IEEE CIS Evolutionary Computation Technical Committee from 2008 - 2010. He was also Chair of the IEEE Computational Intelligence Society Intelligent Systems Applications Technical Committee (ISATC) from 2013-2014. His current research interests include computational intelligence spanning memetic computing, evolutionary optimization using approximation/surrogate/meta-models, complex design optimization, intelligent agents in game, and Big Data Analytics. His research grants comprises of external funding from both national and international partners that include National Grid Office, A*STAR, Singapore Technologies Dynamics, Boeing Research & Development (USA), Rolls-Royce (UK) and Honda Research Institute Europe (Germany), National Research Foundation and MDAGAMBIT. His research work on Memetic Algorithm was featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in August 2007. Recently, he was selected as a 2015 Thomson Reuters Highly Cited Researcher and 2015 World's Most Influential Scientific Minds. He also received the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work pertaining to Memetic Computation.

  

Game Benchmark Competition

Description:

The Game Benchmark for Evolutionary Algorithms (GBEA) is a collection of single- and multi-objective optimisation tasks that occur in applications to games research. We are proposing a competition with multiple tracks that addresses several different research questions all featuring continuous search spaces. The GBEA uses the COCO (COmparing Continuous Optimisers) framework for ease of integration. So please submit your best optimisation algorithms!

- Targets: The task is to find solutions of sufficient quality (as specified by the target) as quickly as possible (measured in number of function evaluations).
- Generality: The task is to achieve good performance across fitness landscapes that use the same fitness function, but with transformed representations.

The competition is further available in a single- and bi-objective version, thus resulting in 4 different tracks. The winners for the above questions will be determined independently.

Why Games?
Games are a very interesting topic that motivates a lot of research and have repeatedly been suggested as testbeds for AI algorithms. Key features of games are controllability, safety and repeatability, but also the ability to simulate properties of real-world problems such as measurement noise, uncertainty and the existence of multiple objectives.

Submission deadline:

Official webpage:

Organizers:

Vanessa Volz

Vanessa Volz is an AI researcher at modl.ai (Copenhagen, Denmark), with focus in computational intelligence in games. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. She holds B.Sc. degrees in Information Systems and in Computer Science from WWU Münster, Germany. She received an M.Sc. with distinction in Advanced Computing: Machine Learning, Data Mining and High Performance Computing from University of Bristol, UK, in 2014. Her current research focus is on employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games.

Tea Tušar

Tea Tusar is a research fellow at the Department of Intelligent Systems of the Jozef Stefan Institute in Ljubljana, Slovenia. She was awarded the PhD degree in Information and Communication Technologies by the Jozef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has completed a one-year postdoctoral fellowship at Inria Lille in France where she worked on benchmarking multiobjective optimizers. Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems.

Boris Naujoks

Boris Naujoks is a professor for Applied Mathematics at TH Köln - Cologne University of Applied Sciences (CUAS). He joint CUAs directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Meanwhile, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of CUAS. He focuses on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and the (industrial) applicability of the explored methods.

  

GECCO 2020 Competition on Niching Methods for Multimodal Optimization

Description:

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 2020 (i.e., submitting via the online submission system of GECCO 2020). The description of the benchmark suite, evaluation procedures, and established baselines can be found in the following technical report:

X. Li, A. Engelbrecht, and M.G. Epitropakis, ``Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization'', Technical Report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 2013.

URL:
https://titan.csit.rmit.edu.au/~e46507/cec13-niching/competition/cec2013-niching-benchmark-tech-report.pdf

The web-site of the previous GECCO competitions (GECCO 2016, 2017, 2018, 2019) can be found here:
http://www.epitropakis.co.uk/gecco2019/
http://www.epitropakis.co.uk/gecco2018/
http://www.epitropakis.co.uk/gecco2017/
http://www.epitropakis.co.uk/gecco2016/

The test suite for the competition and the performance measures are implemented in Matlab, Java, Python and C++, and will be available for download from the competition website (URL will be provided later). Notice that, apart from the benchmark function suite, the competition facilitates the evaluation and comparison of different niching algorithms. The procedures developed takes into consideration two main objectives: 1) the test suite should be simple to use; and 2) the test suite can be used to facilitate fair comparisons of different niching algorithms. The procedure should be easy to follow since user interaction with unnecessary details will be kept at minimal. This will allow interested researchers to focus their effort primarily on the development of state-of-the-art niching algorithms. A framework with such facilities has already proved to be valuable to the research community and has led to major developments of the field, e.g., the Black-Box Optimization Benchmark (BBOB) competition organized at GECCO each year.

We anticipate there will be around 10 competitors to this competition, which
is similar to our successful CEC'2013, CEC'2015, CEC'2016, CEC'2017 and GECCO
2016, GECCO 2017, GECCO 2018, GECCO 2019 competitions.

Submission deadline:

June 30, 2020

Official webpage:

http://epitropakis.co.uk/gecco2020/

Organizers:

Mike Preuss

Mike Preuss is Assistant Professor at LIACS, the computer science institute of Universiteit Leiden in the Netherlands. Previously, he was with ERCIS (the information systems institute of WWU Muenster, Germany), and before with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His main research interests rest on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization, and on computational intelligence and machine learning methods for computer games, especially in procedural content generation (PGC) and realtime strategy games (RTS).

Michael Epitropakis

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, Lancaster University, Lancaster, UK. His current research interests include computational intelligence, evolutionary computation, swarm intelligence, machine learning and search­ based software engineering. He has published more than 35 journal and conference papers. He is an active researcher on Multi­modal Optimization and a co­-organized of the special session and competition series on Niching Methods for Multimodal Optimization. He is a member of the IEEE Computational Intelligence Society and the ACM SIGEVO.

Xiaodong Li

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. 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, machine learning, complex systems, multiobjective optimization, multimodal optimization (niching), 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, a Program Co-Chair for IEEE CEC’2012, a General Chair for ACALCI’2017 and AI’17. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS ""IEEE Transactions on Evolutionary Computation Outstanding Paper Award"".

  

Industrial Challenge

Description:

Similar to the many previous competitions, the team of the Institute of Data Science, Engineering, and Analytics at the TH Cologne, hosts the 'Industrial Challenge' at the GECCO 2020.
As usual, the industrial challenge is posed in cooperation with an industry partner of the institute.
This year's competition partially relies on work presented in last year's GECCO 'Hot of the Press' talk by Daniels et. al. regarding "A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics" -
https://ore.exeter.ac.uk/repository/handle/10871/33078.
The suite provides expensive computer simulation-based optimization problems and provides an easy evaluation interface that will be used for the setup of our challenge.
Our industry partner is willing to publish one of their CFD simulations - the optimization of a gas distribution system (GDS) in a large-scale electrostatic precipitator - as a public challenge. The GDS in the given case consists of 49 slots, each of which can be fitted with multiple types of metal plates, posing a high-dimensional discrete optimization problem.
The simulation will be executed through the previously mentioned interface and hosted on one of our servers (similar to our last year's challenge). This year, we are also planning to host two separate challenge tracks for the first time in the industrial challenge.
The task in the first track is to find an optimal configuration for the gas distribution system with a very limited budget of objective function evaluations.
In the second track, the budget will be unlimited.
In both tracks, the best-found objective function value counts.
There will be multiple versions of the CFD simulation, with slightly differing optimization goals, so that algorithms can be developed and tested before they are submitted for the final evaluation in the challenge.
The participants will be free to apply one or multiple optimization algorithms of their choice.
Thus, we enable each participant to apply his/her algorithms to a real industry problem, without software setup or licensing that would usually be required when working on such problems.

Submission deadline:

June 15, 2020

Official webpage:

Organizers:

Frederik Rehbach

Frederik is a Ph.D. student at the Institute of Data Science, Engineering, and Analytics at the CUAS (Cologne University of Applied Sciences). After earning his bachelor's degree in Electronics as well as a master's degree in Automation & IT, his research is now focused on the parallel application of surrogate model-based optimization.

Thomas Bartz-Beielstein

  • 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.

  

Open Optimization Competition 2020

Description:

The Open Optimization Competition aims at fostering a collective, community-driven effort towards reproducible, open access, and conveniently interpretable comparisons of different optimization techniques, with the goal to support users in choosing the best algorithms and the best configurations for their problem at hand.

The competition has two tracks:
(1) A performance-oriented track, which welcomes contributions of efficient optimization algorithms for the following categories:
- One-shot optimization
- Low budget optimization
- Multi-objective optimization
- Discrete optimization, in particular self-adaptation
- Structured optimization
- Constraint handling
- Algorithm selection and combination

(2) Contributions towards ``better’’ benchmarking, e.g.,
- Performance criteria (for example: how to measure robustness over large families of problems?)
- Visualization of data
- New benchmark problems (e.g., structured optimization problems)
- Cross-validation issues in optimization benchmarking
- Performance statistics
- Software contributions (e.g., efficient distribution over clusters or grids, software contribution in general)
- Mathematically justified improvement, i.e., algorithms or configurations with proven performance guarantees

While the performance track is hosted within Nevergrad, the contributions track welcomes contributions to both Nevergrad and IOHanalyzer, the analytical and visualization module of IOHprofiler. Both tools, Nevergrad and IOHprofiler, are open-source platforms, available on GitHub at https://github.com/facebookresearch/nevergrad and https://github.com/IOHprofiler/IOHanalyzer, respectively. The two tools are linked in that performance data files produced by Nevergrad can be conveniently loaded and analyzed by IOHprofiler, through its web-based interface at http://iohprofiler.liacs.nl/ .

Submission deadline: all pull request made before June 1 are eligible.

Submission deadline:

June 01, 2020

Official webpage:

https://github.com/facebookresearch/nevergrad/blob/master/docs/opencompetition2020.md

Organizers:

Carola Doerr

Carola Doerr, formerly Winzen, is a permanent CNRS researcher at Sorbonne University in Paris, France. Carola's main research activities are in the mathematical analysis of randomized algorithms, with a strong focus on evolutionary algorithms and other black-box optimizers. She has been very active in the design and analysis of black-box complexity models, a theory-guided approach to explore the limitations of heuristic search algorithms. Most recently, she has used knowledge from these studies to prove superiority of dynamic parameter choices in evolutionary computation, a topic that she believes to carry huge unexplored potential for the community. Carola has received several awards for her work on evolutionary computation, among them the Otto Hahn Medal of the Max Planck Society and four best paper awards at GECCO. She is/was program chair of PPSN 2020, FOGA 2019 and the theory tracks of GECCO 2015 and 2017. Carola is an editor of two special issues in Algorithmica. She is also vice chair of the EU-funded COST action 15140 on ``Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)''.

Olivier Teytaud

Olivier Teytaud is research scientist at Facebook. He has been working in numerical optimization in many real-world contexts - scheduling in power systems, in water management, hyperparameter optimization for computer vision and natural language processing, parameter optimization in reinforcement learning. He is currently maintainer of the open source derivative free optimization platform of Facebook AI Research (https://github.com/facebookresearch/nevergrad), containing various flavors of evolution strategies, Bayesian optimization, sequential quadratic programming, Cobyla, Nelder-Mead, differential evolution, particle swarm optimization, and a platform of testbeds including games, reinforcement learning, hyperparameter tuning and real-world engineering problems.

 

Jérémy Rapin

Jérémy Rapin is a research engineer at Facebook. He has been working on signal processing, optimization and deep learning, mostly in the domain of medical imaging. His current focus is on developing nevergrad, an open- source derivative-free optimization platform.

 

Thomas Baeck

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, and then worked for the Informatik Centrum Dortmund (ICD) as department leader of the Center for Applied Systems Analysis. 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 has more than 300 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 the Handbook of Natural Computing, and co-editor-in-chief of Springer's Natural Computing book series. 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 f\""ur Informatik, GI) in 1995 and the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award in 2015.