GECCO 2026 logo

16th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)

“It may be turtles all the way down, but the turtles get smaller.” -- Anonymous @ ECADA 2017

@ GECCO 2026 July 13-17, 2026

Important Dates

March 27, 2026 Workshop paper submission deadline
April 24, 2026 Notification to authors
May 5, 2026 Camera-ready deadline
May 11, 2026 Author's mandatory registration deadline

Workshop Keynote

Niki van Stein

[Portrait photo of Niki van Stein]

Leiden University

 Title: TBD

Abstract:

TBD

Short Bio:

Niki van Stein is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, specializing in Explainable Artificial Intelligence (XAI). Since January 2022, Dr. van Stein has led the XAI research group and is a member of the management team of the Natural Computing cluster. Her research focuses on the intersection of machine learning, LLMs, optimization, and XAI, with applications in predictive maintenance, time-series analysis, and engineering design. Dr. van Stein obtained a PhD in Computer Science from Leiden University in 2018, under the supervision of Prof. Dr. Thomas Bäck, with a thesis on data-driven modelling and optimization of industrial processes.
With over 90 peer-reviewed publications and multiple awards, including best paper recognitions at GECCO and the IEEE Symposium Series on Computational Intelligence, Dr. van Stein has made significant contributions to the fields of evolutionary computing and explainable artificial intelligence.

Related GECCO 2026 Tutorials

Related GECCO 2026 Workshops

Accepted Papers

Workshop Schedule

TBD

Description

The main objective of this workshop is to discuss hyper-heuristics and algorithm configuration methods for the automated generation and improvement of algorithms, with the goal of producing solutions (algorithms) that are applicable to multiple instances of a problem domain. The areas of application of these methods include optimization, data mining and machine learning. [1-18,23].

Automatically generating and improving algorithms by means of other algorithms has been the goal of several research fields, including Artificial Intelligence in the early 1950s, Genetic Programming (GP) since the early 1990s, and more recently automated algorithm configuration [1] and hyper-heuristics [2]. The term hyper-heuristics generally describes meta-heuristics applied to a space of algorithms. While GP has most famously been used to this end, other evolutionary algorithms (EAs) and meta-heuristics have successfully been used to automatically design novel (components of) algorithms. Automated algorithm configuration grew from the necessity of tuning the parameter settings of meta-heuristics and it has produced several powerful (hyper-heuristic) methods capable of designing new algorithms by either selecting components from a flexible algorithmic framework [3,4] or recombining them following a grammar description [5][9].

Although most EAs are designed to generate specific solutions to a given instance of a problem, one of the defining goals of hyper-heuristics is to produce solutions that solve more generic problems. For instance, while there are many examples of EAs for evolving classification models in data mining and machine learning, a GP hyper-heuristic has been employed to create a generic classification algorithm which in turn generates a specific classification model for any given classification dataset, in any given application domain [8]. In other words, the hyper-heuristic operates at a higher level of abstraction compared to how most search methodologies are currently employed; i.e., it is searching the space of algorithms as opposed to directly searching in the problem solution space [9], raising the level of generality of the solutions produced by the hyper-heuristic EA. In contrast to standard GP, which attempts to build programs from scratch from a typically small set of atomic functions, hyper-heuristic methods specify an appropriate set of primitives (e.g., algorithmic components) and allow evolution to combine them in novel ways as appropriate for the targeted problem class. While this allows searches in constrained search spaces based on problem knowledge, it does not limit the generality of this approach as the primitive set can be selected to be Turing-complete. Typically, however, the initial algorithmic primitive set is composed of primitive components of existing high-performing algorithms for the problems being targeted; this more targeted approach very significantly reduces the initial search space, resulting in a practical approach rather than a mere theoretical curiosity. Iterative refining of the primitives allows for gradual and directed enlarging of the search space until convergence.

As meta-heuristics are themselves a type of algorithm, they too can be automatically designed employing hyper-heuristics. For instance, in 2007, GP was used to evolve mate selection in EAs [11]; in 2011, Linear GP was used to evolve crossover operators [12]; more recently, GP was used to evolve complete black-box search algorithms [13,14,16], SAT solvers [22], and FuzzyART category functions [23]. Moreover, hyper-heuristics may be applied before deploying an algorithm (offline) [5] or while problems are being solved (online) [9], or even continuously learn by solving new problems (life-long) [19]. Offline and life-long hyper-heuristics are particularly useful for real-world problem solving where one can afford a large amount of a priori computational time to subsequently solve many problem instances drawn from a specified problem domain, thus amortizing the a priori computational time over repeated problem solving. Recently, the design of Multi-Objective EA components was automated [21].

Very little is known yet about the foundations of hyper-heuristics, such as the impact of the meta-heuristic exploring algorithm space on the performance of the thus automatically designed algorithm. An initial study compared the performance of algorithms generated by hyper-heuristics powered by five major types of GP [18]. Another avenue for research is investigating the potential performance improvements obtained through the use of asynchronous parallel evolution to exploit the typical large variation in fitness evaluation times when executing hyper-heuristics [20].

[1]
Edmund K. Burke, Michel Gendreau, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, & Rong Qu. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695-1724.
[2]
Holger H. Hoos (2012). Automated algorithm configuration and parameter tuning. In Autonomous search (pp. 37-71). Springer Berlin Heidelberg. doi:10.1007/978-3-642-21434-9_3.
[3]
KhudaBukhsh, A. R., Xu, L., Holger H. Hoos & Leyton-Brown, K. (2009). SATenstein: Automatically Building Local Search SAT Solvers from Components. In IJCAI, 9, 517-524.
[4]
Manuel López-Ibáñez and Thomas Stützle. (2012). The automatic design of multiobjective ant colony optimization algorithms. IEEE Transactions on Evolutionary Computation, 16(6):861-875.
[5]
Mascia, F., Manuel López-Ibáñez, Dubois-Lacoste, J., & Thomas Stützle. (2014). Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools. Computers & operations research, 51, 190-199.
[6]
William B. Langdon and Mark Harman. Genetically Improving 50000 Lines of C++. Research Note , RN/12/09, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK, 2012.
[7]
Justyna Petke, Mark Harman, William B. Langdon, and Westley Weimer. Using Genetic Improvement & Code Transplants to Specialise a C++ Program to a Problem Class Proceedings of the 17th European Conference on Genetic Programming, EuroGP 2014, Granada, Spain, 2014. Springer Verlag.
[8]
Gisele L. Pappa and Alex A. Freitas. Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach, Springer, Natural Computing Series, 2010.
[9]
Edmund K. Burke, Matthew Hyde, Graham Kendall and John Woodward. A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. In IEEE Transactions on Evolutionary Computation, 14(6):942-958, December 2010.
[10]
M. Oltean and D. Dumitrescu. Evolving TSP heuristics using multi expression programming. In: Computational Science - ICCS 2004, Lecture Notes in Computer Science 3037, pp. 670-673. Springer, 2004.
[11]
Ekaterina A. Smorodkina and Daniel R. Tauritz. Toward Automating EA Configuration: the Parent Selection Stage. In Proceedings of CEC 2007 - IEEE Congress on Evolutionary Computation, pages 63-70, Singapore, September 25-28, 2007.
[12]
Brian W. Goldman and Daniel R. Tauritz. Self-Configuring Crossover. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '11), pages 575-582, Dublin, Ireland, July 12-16, 2011.
[13]
Matthew A. Martin and Daniel R. Tauritz. Evolving Black-Box Search Algorithms Employing Genetic Programming. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '13), pages 1497-1504, Amsterdam, The Netherlands, July 6-10, 2013.
[14]
Matthew A. Martin and Daniel R. Tauritz. A Problem Configuration Study of the Robustness of a Black-Box Search Algorithm Hyper-Heuristic. In Proceedings of the 16th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '14), pages 1389-1396, Vancouver, BC, Canada, July 12-16, 2014.
[15]
John R. Woodward and Jerry Swan, "The automatic generation of mutation operators for genetic algorithms", in Proceedings of the 14th international conference on Genetic and evolutionary computation conference, 2012.
[16]
Matthew A. Martin and Daniel R. Tauritz. Hyper-Heuristics: A Study On Increasing Primitive-Space. In Proceedings of the 17th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '15), pages 1051-1058, Madrid, Spain, July 11-15, 2015.
[17]
Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan. Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming. IEEE Transactions on Evolutionary Computation, 18(2):193-208, April 2014.
[18]
Sean Harris, Travis Bueter, and Daniel R. Tauritz. A Comparison of Genetic Programming Variants for Hyper-Heuristics. In Proceedings of the 17th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '15), pages 1043-1050, Madrid, Spain, July 11-15, 2015.
[19]
Kevin Sim, Emma Hart, and Ben Paechter. A Lifelong Learning Hyper-Heuristic for Bin-Packing Evolutionary Computation, MIT Press Evolutionary Computation, 23(1):37-67, March 2015.
[20]
Alex R. Bertels, and Daniel R. Tauritz. Why Asynchronous Parallel Evolution is the Future of Hyper-heuristics: A CDCL SAT Solver Case Study. In Proceedings of the 18th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '16), pages 1359-1365, Denver, Colorado, U.S.A., July 20-24, 2016.
[21]
Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatic Component-Wise Design of Multi-Objective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 20(3):403-417, 2016.
[22]
Marketa Illetskova, Alex R. Bertels, Joshua M. Tuggle, Adam Harter, Samuel Richter, Daniel R. Tauritz, Samuel Mulder, Denis Bueno, Michelle Leger and William M. Siever. Improving Performance of CDCL SAT Solvers by Automated Design of Variable Selection Heuristics. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017), Honolulu, Hawaii, U.S.A., November 27 - December 1, 2017.
[23]
Islam Elnabarawy, Daniel R. Tauritz and Donald C. Wunsch. Evolutionary Computation for the Automated Design of Category Functions for Fuzzy ART: An Initial Exploration. In Proceedings of the 19th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '17), pages 1133-1140, Berlin, Germany, July 15-19, 2017.

Call for Papers

We welcome original submissions on all aspects of Evolutionary Computation for the Automated Design of Algorithms, in particular, evolutionary computation methods and other hyper-heuristics, including hybridization with LLMs, for the automated design, generation or improvement of algorithms that can be applied to any instance of a target problem domain. Relevant methods include methods that evolve whole algorithms given some initial components as well as methods that take an existing algorithm and improve it or adapt it to a specific domain. Another important aspect of automated algorithm design is the definition of the primitives that constitute the search space of hyper-heuristics. These primitives should capture the knowledge of human experts about useful algorithmic components (such as selection, mutation and recombination operators, local searches, etc.) and, at the same time, allow the generation of new algorithm variants. Examples of the application of hyper-heuristics, including GP and automatic configuration methods, to such frameworks of algorithmic components, are of interest to this workshop, as well as the (possibly automatic) design of the algorithmic components themselves and the overall architecture of metaheuristics. Therefore, relevant topics include (but are not limited to):

Paper Submission

Workshop papers must be submitted using the GECCO submission site. After login, the authors need to make sure that their role is set to "Submitter", select "Make a New Submission", then select "Workshop Paper", and then in the workshop submission form, the authors must select the workshop they are submitting to, namely "Workshop Evolutionary Computation for the Automated Design of Algorithms".

Submitted papers must not exceed 8 pages (excluding references) and are required to be in compliance with the GECCO 2026 Paper Submission Instructions. It is recommended to use the same templates as the papers submitted to the main tracks. Each paper submitted to this workshop will be rigorously reviewed in a double-blind review process. In other words, authors should not know who the reviewers of their work are and reviewers should not know who the authors are. To this end, the following information is very important: Submitted papers should be ANONYMIZED. This means that they should NOT contain any element that may reveal the identity of their authors. This includes author names, affiliations, and acknowledgments. Moreover, any references to any of the author's own work should be made as if the work belonged to someone else. All accepted papers will be presented at the ECADA workshop and appear in the GECCO 2026 Conference Companion Proceedings. By submitting a paper, the author(s) agree that, if their paper is accepted, they will:

As a published ACM author, you and your co-authors are subject to all ACM Publications Policies (https://www.acm.org/publications/policies/toc), including ACM's new Publications Policy on Research Involving Human Participants and Subjects (https://www.acm.org/publications/policies/research-involving-human-participants-and-subjects).

Program Committee

TBD

Co-Chairs (in alphabetical order)

[Portrait photo of Emma Hart]

E-mail: e.hart@napier.ac.uk

Emma Hart is a Professor in Computer Science at Edinburgh Napier University where she is Chair of Natural Computation. She gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems.

She is active world-wide in the field of Evolutionary Computation, for example as General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She has given keynotes a CLAIO 2022, WCCI 2022, CEC 2019, EURO 2016 and UKCI 2015, as well as invited talks and tutorials at many Universities and international conferences. She was Editor-in-Chief of Evolutionary Computation (MIT Press) from 2017 through 2023, and an elected member and vice-chair of the ACM SIGEVO Executive Board. She is also a member of the UK Operations Research Society Research Panel. In 2022 she was elected as a Fellow of the Royal Society of Edinburgh for contributions to Computational Intelligence. In 2023, she was awarded the ACM SIGEVO Outstanding Contribution Award.
[Portrait photo of Gisele Pappa]

E-mail: glpappa@dcc.ufmg.br

Gisele Pappa is a Professor in the at the Federal University of Minas Gerais (UFMG) in Brazil. Her main research interests are the intersection of the areas of machine learning and evolutionary computation, with a special interest in genetic programming and its applications in classification and regression tasks. She has also been actively researching the use of EAs for automated machine learning, focusing on applications for health data and also fraud detection.
[Portrait photo of Daniel Tauritz]

E-mail: dtauritz@acm.org

Daniel R. Tauritz is the COLSA Endowed Professor in the Department of Computer Science and Software Engineering in the Samuel Ginn College of Engineering at Auburn University (AU) in the USA, AU's Director for National Laboratory Relationships, the founding Head of AU's Biomimetic Artificial Intelligence Research Group (BioAI Group), a consultant for Sandia National Laboratories, and a Guest Scientist at Los Alamos National Laboratory (LANL). He received his Ph.D. in Computer Science in 2002 from Leiden University.

For GECCO, he served previously as Late Breaking Papers Chair in 2010, GA Track Co-Chair in 2012 & 2013, co-instructor of the hyper-heuristics tutorial from 2015 through 2024, co-chair of the ECADA workshop every year since 2015, and member of the GA track program committee for many years. He has also served on a variety of other international conference program committees, including the IEEE Congress on Evolutionary Computation (CEC) and Parallel Problem Solving from Nature (PPSN). His research interests include the design of generative hyper-heuristics, parameter control in evolutionary algorithms, competitive coevolutionary algorithms including their real-world application in security and defense, and computational evolution, in particular evolutionary algorithms for simulating molecular evolution.

Previous ECADA workshops