GECCO 2021 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).

In addition to the competitions listed on this page, GECCO also hosts the Humies Award for human-competitive results produced by genetic and evolutionary computation.

### Information for authors of "2-page Competition Abstracts"

**The submission system is still closed for Competition Abstracts**

After logging into GECCO's submission site https://ssl.linklings.net/conferences/gecco/, click on "Make a new submission", then selection "Competition Entry", and then select the respective competition.

Note that not all competitions offer this (if in doubt, please check with the respective competition organisers). The submission is (in general) voluntary, except when explicitly made mandatory by a competition.

### List of Competitions

## "Continuous" Interaction Testing

### Description:

Interaction testing allows for determining a fault in a complex engineered system where a fault is the result of a "small" number of factors interacting. Typically, the object of study is a "covering array", which is an array of symbols (a given number of rows and columns) that encodes the tests to-be-performed: the rows are the tests, and the columns correspond to the factors/components of the system. The property of the array is that all interactions of size at most a given number (the "strength") appear in the array at least once.

One main question to ask is: for a given system, when does a covering array exist? The most common goal in interaction testing is minimizing the number of rows/tests while maintaining the "coverage" property. It can be shown that as the number of factors increases, the minimum number of rows must increase.

Showing a covering array existing is easy, but proving that one does not exist is very difficult, and often mathematicians result to case analyses to prove an array cannot exist.

This competition is focused on a "continuous" generalization of covering arrays, which appear to allow (1) a more fine-grained approach to understand how "covering" an array is, and (2) possibly a better explanation for why certain arrays do not exist.

Let $N, t, k, v$ be positive integers such that $k \ge t$ (to avoid trivial cases).

A \textit{covering array} $CA_\lambda(N; t, k, v)$ is a 2-dimensional array with $N$ rows, $k$ columns, and every entry is one value from $\{1, \cdots, v\}$.

Additionally, for every choice of $t$ distinct columns $c_1, \cdots, c_t$, and every choice of $t$ values $v_1, \cdots, v_t$ from the set $\{1, \cdots, v\}$, there exists some row $R$ such that $Rc_j = v_j$ for all $1 \le j \le t$.

Additionally, let $\varepsilon$ be a nonnegative real number, and $f : \mathbb{R} \times \{1, \cdots, v\}^t \to \mathbb{R}_{\ge 0}$ be a function that takes a $t$-tuple $(v_1, \cdots, v_t)$ of real numbers, and a $t$-tuple of \textit{integers} from the set $\{1, \cdots, v\}$, and outputs a nonnegative real number.

A \textit{continuous covering array} is a 2-dimensional array with $N$ rows and $k$ columns.

However, here each entry is a \textit{real number}.

In general, the array has a property that depends on $f$, and the property holds if a numerical calculation is less than the parameter $\varepsilon$.

There are two types of continuous covering arrays that we will consider: \textit{max}, and \textit{sum}.

For every choice of $t$ distinct columns $C = (c_1, \cdots, c_t)$, and every choice of $t$ values $V = (v_1, \cdots, v_t)$, let $\rho(C,V)$ be:

\[

\rho(C,V) = \min_{1 \le R \le N} f(RC, V).

\]

In other words, $\rho(C,V)$ is the minimum $f$ value given the columns of $C$, across all rows.

For the purposes of this competition, we will use the Euclidean distance for $f$:

\f(X, Y) = \sqrt{\sum_{i=1}^t (X_i - Y_i)^2}

\

where $X = (X_1, \cdots, X_t), Y = (Y_1, \cdots, Y_t)$.

We define a "max" continuous covering array. Define the value $M$ as follows:

\C

In English, $M$ is the maximum distance between any interaction and its corresponding $\rho$ value.

We say that the array is a \textit{max covering array} if $M < \varepsilon$.

Note that $\varepsilon = 0$ if and only if a ``standard'' covering array exists with $N$ rows, $k$ columns, $v$ values, and strength $t$.

We define a "sum" continuous covering array. Define $S$ as follows:

\C

Then we say that the array is a \textit{sum covering array} if $S < \varepsilon$.

Note that $\varepsilon = 0$ if and only if a ``standard'' covering array exists with $N$ rows, $k$ columns, $v$ values, and strength $t$.

Finally, we provide the test instances for this competition. For each, we describe whether or not the "standard" covering array exists or doesn't (or is not known to), and if it does exist, whether or not it is unique up to isomorphism (permutation of rows, columns, or values within a column). For appropriately large epsilon values, the "continuous" array exists, however.

1. $N = 16, t=2, k=5, v=4$ (exists, unique)

2. $N=6, t=2, k=10, v=2$ (exists, unique)

3. $N=15, t=2, k=20, v=3$ (exists, not unique)

4. $N=17, t=3, k=16, v=2$ (exists, not unique)

5. $N=127, t=3, k=21, v=4$ (exists, not unique)

6. $N=237, t=4, k=17, v=3$ (exists, not unique)

7. $N=3, t=2, k=3, v=2$ (does not exist)

8. $N=14, t=3, k=12, v=2$ (does not exist)

9. $N=38, t=3, k=7, v=3$ (does not exist)

10. $N=126, t=3, k=7, v=5$ (does not exist)

11. $N=100, t=2, k=5, v=10$ (unknown to exist)

12. $N=230, t=3, k=5, v=6$ (unknown to exist)

13. $N=60, t=5, k=13, v=2$ (unknown to exist)

14. $N=180, t=3, k=10, v=5$ (unknown to exist)

Important Dates:

March 15, 2021: Deadline to register for the competition and submission of abstract for publication in the GECCO companion.

April 10, 2021: Deadline for submitting abstracts and solutions.

May 1, 2021: Notification of acceptance for GECCO companion.

May 10, 2021: Deadline for general registration for the competition.

June 5, 2021: Deadline for general submission of solutions and approaches.

July 10-14, 2021: GECCO 2021 conference, and announcement of winners and results.

### Submission deadline:

2021-06-05### Official webpage:

### Organizers:

#### Ryan Dougherty

#### Xi Jiang

Xi (Chase) Jiang is an undergraduate student in Computer Science and Quantitative Economics at Colgate University. Jiang is conducting research in network efficiency, correctness, and verification, such as heterogeneous SDN routing protocol implementation interoperability. He also shares research interests in software testing and IoT security and privacy. Jiang is a member of the Colgate Coders club and also a recipient of the Colgate Dean’s Award with Distinction for Academic Excellence. He will be graduating in May and is currently applying for Ph.D. positions in related fields.

## Bound Constrained Single Objective Numerical Optimization

### Description:

In this competition, participants will test their single objective numerical optimization algorithms on a selected 10D and 30D problems with / without a number of transformations such as shifting, rotation, shearing, etc.

### Submission deadline:

2021-04-30### Official webpage:

https://www3.ntu.edu.sg/home/epnsugan/index_files/cec-benchmarking.htm### Organizers:

#### Ponnuthurai Suganthan

Ponnuthurai Nagaratnam Suganthan finished schooling at Union College and subsequently 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. He received an honorary doctorate (i.e. Doctor Honoris Causa) in 2020 from University of Maribor, Slovenia. 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 IEEE Trans. on SMC: Systems (2020 - ). He is a founding co-editor-in-chief of Swarm and Evolutionary Computation (2010 - ), an SCI Indexed Elsevier Journal. His research interests include swarm and evolutionary algorithms, pattern recognition, forecasting, randomized neural networks, deep learning and applications of swarm, evolutionary & machine learning algorithms. He was selected as one of the highly cited researchers by Thomson Reuters every year from 2015 to 2020 in computer science. He served as the General Chair of the IEEE SSCI 2013. He is an IEEE CIS distinguished lecturer (DLP) in 2018-2021.

## 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 (i.e., submitting via the online submission system of GECCO), or to hand in a GECCO short paper on their competition entry. 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.

### Submission deadline:

2021-06-24### Official webpage:

### Organizers:

#### Assistant Prof. 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).

#### Dr. Michael G. 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 Multi≠modal 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"""".

#### Jonathan Fieldsend

Jonathan Fieldsend is Professor of Computational Intelligence at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, and has been working on uncertain problems in evolutionary computation since 2005. This strand of work has mainly been in multi-objective data-driven problems, although more recently has undertaken work on expensive uni-objective robust problems. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Force on Data-Driven Evolutionary Optimisation of Expensive Problems, and sits on the IEEE CIS Task Force on Multi-modal Optimisation and the IEEE CIS Task Force on Evolutionary Many-Objective Optimisation. Alongside EAPwU, he has been a co-organiser of the VizGEC and SAEOpt workshops at GECCO for a number of years.

## 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 (7), 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 (1,2,6) and real-world (4,6) 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.

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.

(1) 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.

(2) 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.

(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) Weibo Lin, Fuda Ma, Zhouxing Su, Qingyun Zhang, Chumin Li, and Zhipeng Lü. Weighting-based parallel local search for optimal camera placement and unicost set covering. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, GECCO ’20, pages 3–4, New York, NY, USA, 2020. Association for

Computing Machinery.

(7) 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.

### Submission deadline:

2021-06-03### Official webpage:

### 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.

#### 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.

#### 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.

## 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. A hero posses certain 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 with 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:

2021-06-25### 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 evolutionary computation 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. He received the Humies Silver medial with Stephen Kelly in 2018 for evolving human (and deep learning) competitive solutions to the Arcade Learning Environment at a fraction of the computational cost.

#### Alexandru Ianta

Alexandru Ianta is currently a Master's student at the Faculty of Computer Science at Dalhousie University, Canada. He has constructed both back end and front end code for this competition.

## 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.

This competition extends the 2020 Dynamic Stacking competition with a second track.

The first track is similar to the 2020 competition whereby 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. 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. A range of performance indicators will be used to determine the winner.

The second track represents another stacking scenario that is derived from real-world scenarios. It features two cranes and two different handovers. The cranes have a capacity of larger than one which represents an additional challenge for the solver. The solver may just provide the moves and the cranes will sort out the order in which these are performed (not optimal though) or the solver may optimize both the moves and the assignment and schedule of the cranes. In this scenario, not the arrival stack is the critical part, but the handover stacks and thus the downstream process must not run empty.

The dynamic environments are 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. As in the 2020 competition a website will be used that participants can use to create experiment and test their solvers.

### Submission deadline:

2021-06-26### 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 Leitner

Sebastian Leitner (né 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.

#### Johannes Karder

Johannes Karder received his master's degree in software engineering in 2014 from the University of Applied Sciences Upper Austria and is a research associate in the Heuristic and Evolutionary Algorithms Laboratory at the Research Center Hagenberg. His research interests include algorithm theory and development, simulation-based optimization and optimization networks. He is a member of the HeuristicLab architects team. He is currently pursuing his PhD in technical sciences at the Johannes Kepler University, Linz, where he conducts research on the topic of dynamic optimization problems.

#### Bernhard Werth

Bernhard Werth received his MSc in computer science in 2016 from Johannes Kepler University Linz, Austria. He works as a researcher at the R&D facility at University of Applied Sciences Upper Austria, Hagenberg Campus. Mr Werth is contributor to the open source software environment HeuristicLab and member of the Heuristic and Evolutionary Algorithms Laboratory (HEAL) research group led by Dr. Affenzeller. He has authored and co-authored several papers concerning evolutionary algorithms, fitness landscape analysis, surrogate-assisted optimization and data quality monitoring.

## Evolutionary Computation in the Energy Domain: Smart Grid Applications

### Description:

Following the success of the previous editions (CEC, GECCO, WCCI), we are launching a more challenging competition at major conferences in the field of computational intelligence. This GECCO 2021 competition proposes two testbeds in the energy domain:

Testbed 1) 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.

Testbed 2) Flexibility management of home appliances to support DSO requests. A model for aggregators flexibility provision in distribution networks that takes advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) is proposed. The problem can be modeled as a Mixed-Integer Non-Linear Programming (MINLP) in which the aggregator strives to match a flexibility request from the DSO/BRP, paying a remuneration to the households participating in the DR program according to their preferences and the modification of their baseline profile.

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

Competition goals:

Following the success of the previous editions (CEC, GECCO, WCCI), we are launching a more challenging competition at major conferences in the field of computational intelligence. This GECCO 2021 competition proposes two tracks in the energy domain:

Track 1) 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 track) , 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.

Track 2) Flexibility management of home appliances to support DSO requests. A model for aggregators flexibility provision in distribution networks that takes advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) is proposed. The problem can be modeled as a Mixed-Integer Non-Linear Programming (MINLP) in which the aggregator strives to match a flexibility request from the DSO/BRP, paying a remuneration to the households participating in the DR program according to their preferences and the modification of their baseline profile.

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

Competition goals:

The GECCO 2021 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 optimal bidding of energy aggregators in local markets and the Flexibility management of home appliances to support DSO requests. 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 each independent problem 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.

### Submission deadline:

2021-05-30### Official webpage:

http://www.gecad.isep.ipp.pt/ERM-Competitions (will be online if accepted)### Organizers:

#### Fernando Lezama

Fernando Lezama received the Ph.D. in ICT from the ITESM, Mexico, in 2014. Since 2017, he is a

researcher at GECAD, Polytechnic of Porto, where he contributes to the application of computational intelligence (CI) in the energy domain. Dr. Lezama is part of the National System of Researchers of

Mexico since 2016, Chair of the IEEE CIS TF 3 on CI in the Energy Domain, and has been involved in the organization of special sessions, workshops, and competitions (at IEEE WCCI, IEEE CEC and ACM GECCO), to promote the use of CI to solve complex problems in the energy domain.

#### Joao Soares

João Soares attained his Ph.D. degree in Electrical and Computer Engineering from UTAD University in early 2017. He currently conducts research at GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, in the School of Engineering of the Polytechnic of Porto and has been an invited visiting professor at Ecole Centrale de Lille - L2EP since 2019. He has contributed to more than 120 publications in the field of power systems and computational intelligence. His works have been cited over 2500 times (H-index 26 in google scholar). He has participated in several national and international research funded projects and since 2018 he is involved in the coordination of two FCT projects concerning energy resource management in smart grids (PTDC/EEI-EEE/28983/2017) and smart buildings (PTDC/EEI-EEE/29070/2017). Currently, he is vice-chair of IEEE Computational Intelligence Society (CIS) Taskforce 3 and has been contributing to bridging the gap between computational intelligence and power system engineers, namely with numerous efforts, in particular with dedicated algorithm competitions in international specialized venues since 2017.

#### 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 (Senior Member, IEEE) received the Ph.D. degree in electrical and computer engineering from the University of Porto, Porto, Portugal, in 1993. She is currently a Professor with the Polytechnic Institute of Porto, Porto. Her research interests focus on artificial intelligence applications, smart grids, electricity markets, demand response, electric vehicles, and renewable energy sources.

#### Ruben Romero

Ruben Romero (Senior Member, IEEE) received the B.Sc. and P.E. degrees from the National University of Engineering, Lima, Peru, in 1978 and 1984, respectively, and the M.Sc. and Ph.D. degrees from the University of Campinas, Campinas, Brazil, in 1990 and 1993, respectively. He is currently a Professor of electrical engineering with São Paulo State University, Ilha Solteira, Brazil. His research interests include methods for the optimization, planning, and control of electrical power systems, applications of artificial intelligence in power systems, and operations research.

## 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 featuring continuous and integer search spaces. The GBEA uses the COCO (COmparing Continuous Optimisers) framework for ease of integration.

The task is to find solutions of sufficient quality (as specified by a target value) as quickly as possible. The competition is available in a single- and bi-objective version for two different applications, thus resulting in 4 different tracks. Details will be available on our website.

Participants will be able to submit short algorithm descriptions as 2-page contributions to the GECCO Companion. The deadline for submissions will be in April 2021.

== 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.

The motivation for the competition setup is as follows. If an algorithm for generating content (such as a Mario level or a Top Trumps deck) is integrated in a game, the goal is usually to provide replay value by varying the content. In this context, it is not necessary to find the single solution that optimises the designer's objectives, but instead, it is important that a "good enough" solution can be found as fast as possible. "Good enough" in this case can be defined in relation to the values achieved by a baseline algorithm. Additionally, ideally, the same algorithm can find solutions across different objectives.

So which game levels can you find? Let us find out! Submit your best optimisation algorithms!

### Submission deadline:

2021-05-31### Official webpage:

http://www.gm.fh-koeln.de/~naujoks/gbea/### 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.

## Minecraft Open-Endedness Competition

### Description:

The purpose of this first contest on open-endedness is to highlight the progress in algorithms that can create novel and increasingly complex artefacts. While most experiments in open-ended evolution have so far focused on simple toy domains, we believe Minecraft - with its almost unlimited possibilities - is the perfect environment to study and compare such approaches. While other popular Minecraft competitions, like MineRL, have an agent-centric focus, in this competition the goal is to directly evolve Minecraft builds.

As part of this competition, we introduce the Minecraft Mechanical Creations Environment (MMCE) API. The MMCE is implemented as a mod for Minecraft that allows clients to manipulate blocks in a running Minecraft server programmatically through an API. The framework is specifically developed to facilitate experiments in artificial evolution. The competition framework also supports the recently added "redstone" circuit components in Minecraft, which allowed players to build amazing functional structures, such as bridge builders, battle robots, or even complete CPUs. Can an open-ended algorithm running in Minecraft discover similarly complex artefacts automatically?

In contrast to the Minecraft Settlement Generation Challenge, this competition is more about - but not exclusively focused on - the evolution of mechanical/functional artefacts.

### Submission deadline:

2021-05-31### Official webpage:

### Organizers:

#### Djordje Grbic

Postdoc at IT University of Copenhagen, researching safety in reinforcement learning, self-driving vehicles and artificial life. Did his PhD at the University of Geneva in Switzerland specializing in bioinformatics, genetic algorithms and machine learning applied to studies in evolutionary biology.

#### Rasmus Berg Palm

Postdoc at IT University of Copenhagen, researching unsupervised learning of object oriented world models. Did his PhD at the Technical University of Denmark in end-to-end document understanding.

#### Elias Najarro

Research Assistant at the Robotics, Evolution, and Art Lab (REAL), IT University of Copenhagen. Working on meta-learning, evolutionary computation and open-endedness.

#### Claire Glanois

Postdoc in deep reinforcement learning at Shanghai Jiao Tong- University of Michigan Joint Institute, notably around neural-logic, multi-agents, and neuro-evolution. Previous research in pure mathematics, with a PhD from University Paris 6, in number theory around the algebraic structure of motivic periods.

#### Sebastian Risi

Professor at the IT University of Copenhagen where he co-directs the Robotics, Evolution and Art Lab (REAL). He is currently the principal investigator of a Sapere Aude: DFF Starting Grant (Innate: Adaptive Machines for Industrial Automation). He has won several international scientific awards, including multiple best paper awards, the Distinguished Young Investigator in Artificial Life 2018 award, a Google Faculty Research Award in 2019, and an Amazon Research Award in 2020. More information: sebastianrisi.com

## Open Optimization Competition 2021: Competition and Benchmarking of Sampling-Based Optimization Algorithms

### Description:

In order to promote research in black-box optimization, we organize a competition around Nevergrad (https://facebookresearch.github.io/nevergrad/index.html ) and IOHprofiler (https://iohprofiler.github.io/ ).

The competition has two tracks:

Track 1: Performance-Oriented Track: Contributors submit an optimization algorithm as a pull request in Nevergrad as detailed below. Several subtracks (“benchmark suites”) are available, covering a broad range of black-box optimization scenarios, from discrete over mixed-integer to continuous optimization, from “artificial” academic functions to real-world problems, from one-Shot Setting over sequential optimization to parallel settings.

Track 2: General Benchmarking Practices: contributions are made by pull request to Nevergrad or IOHprofiler and are accompanied by a “paper style” documentation. This track invites contributions to all aspects of benchmarking black-box optimization algorithms, e.g., by suggesting new benchmark problems, performance measures, or statistics, by extending or improving the functionalities of the benchmarking environment, etc.

### Submission deadline:

2021-06-30### Official webpage:

(will be provided upon acceptance, here is the one from last year: https://facebookresearch.github.io/nevergrad/opencompetition2020.html )### 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 sampling-based optimization heuristics. 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 associate editor of ACM Transactions on Evolutionary Learning and Optimization, editorial board member of the Evolutionary Computation journal, advisory board member of the Springer Natural Computing Book Series. She was program chair of PPSN 2020, FOGA 2019, and the theory tracks of GECCO 2015 and 2017. Carola was guest 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)''. She is a founding and coordinating member of the \href{https://sites.google.com/view/benchmarking-network/}{Benchmarking Network}, an initiative created to consolidate and to stimulate activities on benchmarking sampling-based optimization heuristics, she has organized several workshops on benchmarking, and was co-organizer of the \href{https://facebookresearch.github.io/nevergrad/opencompetition2020.html}{Open Optimization Competition 2020}.

#### 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 Bäck

Thomas Bäck is professor of Computer Science at the Leiden Institute of Advanced Computer Science, Leiden University, Netherlands, since 2002. He received his PhD in Computer Science from Dortmund University, Germany, in 1994, and was leader of the Center for Applied Systems Analysis at the Informatik Centrum Dortmund until 2000. Until 2009, Thomas was also CTO of NuTech Solutions, Inc.~(Charlotte, NC), where he gained ample experience in solving real-world problems in optimization and data analytics, by working with global enterprises in the automotive and other industry sectors. Thomas received the IEEE Computational Intelligence Society (CIS) Evolutionary Computation Pioneer Award for his contributions in synthesizing evolutionary computation (2015), was elected as a fellow of the International Society of Genetic and Evolutionary Computation (ISGEC) for fundamental contributions to the field (2003), and received the best dissertation award from the ""Gesellschaft für Informatik"" in 1995. Thomas has more than 300 publications, 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.

## Optimization of a simulation model for a capacity and resource planning task for hospitals under special consideration of the COVID-19 pandemic

### Description:

Similar to the many previous competitions, the team of the Institute of Data Science, Engineering, and Analytics at the TH Cologne (IDE+A), hosts the 'Industrial Challenge' at the GECCO 2021.

This year’s industrial challenge is posed in cooperation with an IDE+A partner from health industry and with Bartz & Bartz GmbH.

Simulation models are valuable tools for resource usage estimation and capacity planning. Your goal is to determine improved simulation model parameters for a capacity and resource planning task for hospitals. The simulator, babsim.hospital, explicitly covers difficulties for hospitals caused by the COVID-19 pandemic. The simulator can handle many aspects of resource planning in hospitals:

- various resources such as ICU beds, ventilators, personal protection equipment, staff, pharmaceuticals

- several cohorts (based on age, health status, etc.).

The task represents an instance of an expensive, high-dimensional computer simulation-based optimization problem and provides an easy evaluation interface that will be used for the setup of our challenge. The simulation will be executed through an interface and hosted on one of our servers (similar to our last year's challenge).

The task is to find an optimal parameter configuration for the babsim.hospital simulator with a very limited budget of objective function evaluations. The best-found objective function value counts. There will be multiple versions of the babsim.hospital simulations, 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 problem from health industry, without software setup or licensing that would usually be required when working on such problems.

### Submission deadline:

2021-06-30### Official webpage:

### Organizers:

#### Margarita Rebolledo

#### 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.

#### Sowmya Chandrasekaran

M. Eng. Sowmya Chandrasekaran is a research associate at Institute for Data Science, Engineering and Analytics TH Köln, Germany. Her research interest includes: Computational Intelligence, Multivariate Anomaly Detection in Sensor Data, Performance Analysis Frameworks, Data Mining/ Machine Learning, Process Mining, Deep Learning and Internet of Things.

#### 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.

## Real-World Multi-Objective Optimization Competition

### Description:

In this competition, participants will solve a collection of real-world multi-objective optimization problems using their algorithms. The results can be submitted as a two page long competition paper. Detailed results can be placed online and submitted to

### Submission deadline:

2020-04-30### Official webpage:

https://www3.ntu.edu.sg/home/epnsugan/index_files/cec-benchmarking.htm### Organizers:

#### Ponnuthurai Suganthan

Ponnuthurai Nagaratnam Suganthan finished schooling at Union College and subsequently 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. He received an honorary doctorate (i.e. Doctor Honoris Causa) in 2020 from University of Maribor, Slovenia. 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 IEEE Trans. on SMC: Systems (2020 - ). He is a founding co-editor-in-chief of Swarm and Evolutionary Computation (2010 - ), an SCI Indexed Elsevier Journal. His research interests include swarm and evolutionary algorithms, pattern recognition, forecasting, randomized neural networks, deep learning and applications of swarm, evolutionary & machine learning algorithms. He was selected as one of the highly cited researchers by Thomson Reuters every year from 2015 to 2020 in computer science. He served as the General Chair of the IEEE SSCI 2013. He is an IEEE CIS distinguished lecturer (DLP) in 2018-2021.

## The AbstractSwarm Multi-Agent Logistics Competition

### Description:

This competition aims to motivate work in the broad field of logistics. We have prepared a benchmarking framework which allows the development of multi-agent swarms to process a variety of test environments. Those can be extremely diverse, highly dynamic and variable of size. The ultimate goal of this competition is to foster comparability of multi-agent systems in logistics-related problems (e. g., in hospital logistics). Many such problems have good accessibility and are easy to comprehend, but hard to solve. Problems of different difficulty have been designed to make the framework interesting for educational purposes. However, finding efficient solutions for different a priori unknown test environments remains a challenging task for practitioners and researchers alike.

Following these ideas, in the AbstractSwarm Multi-Agent Logistics Competition, participants must develop agents that are able to cooperatively solve different a priori unknown logistics problems. A logistics problem is given as a graph containing agents and stations. An agent can interact with the graph (1) by deciding which station to visit next, (2) by communicating with other agents, and (3) by retrieving a reward for its previous decision. While simulating a scenario, a timetable in the form of a Gantt-chart is created according to the decisions of all agents. Submissions will be ranked according to the total number of idle time of all agents in several different a priori unknown problem scenarios in conjunction with the number of iterations needed to come to the solution.

### Submission deadline:

2021-05-14### Official webpage:

https://abstractswarm.gitlab.io/abstractswarm_competition/### Organizers:

#### Daan Apeldoorn

Daan Apeldoorn primarily works for the Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI) in the Medical Informatics department at the University Medical Centre of the Johannes Gutenberg University Mainz, Germany, and additionally for the Z Quadrat GmbH in Mainz. His research focuses on the extraction and exploitation of knowledge bases in the context of learning agents. He is also active in the field of multi-agent systems with application in (hospital) logistics. In the past, he worked as a scientific staff member for the TU Dortmund University and the University of Koblenz-Landau.

#### Alexander Dockhorn

Alexander Dockhorn is a post-doctoral research associate at the Queen Mary University of London. He received his PhD at the Otto von Guericke University in Magdeburg in 2020. His current research focuses on state and action abstraction methods for competitive strategy game AI. He is active member of the IEEE in which he serves as the chair of the IEEE CIS Competitions Sub-Committee and recently joined as a member of the Games Technical Committee (GTC). Since 2017, he is organizing the Hearthstone AI competition to foster comparability of AI agents in card games.

#### Lars Hadidi

Lars Hadidi is a theoretical physicist working as a research fellow at the Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI) in the Medical Informatics department at the University Medical Centre of the Johannes Gutenberg University Mainz, Germany. He is currently focusing on neural networks which directly operate on graph structured data, their methods and applications.

#### Torsten Panholzer

Torsten Panholzer is managing director of the division Medical Informatics at the Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI) at the University Medical Centre Mainz, Germany. He studied natural sciences and graduated as PhD at the Johannes Gutenberg University Mainz. His research focus is on system and data integration, identity management and artificial intelligence.