13th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)

@ GECCO 2023 July 15-19, 2023

Important Dates

Related GECCO 2023 Tutorials

• Generative Hyper-heuristics by Daniel R. Tauritz and John R. Woodward
• Automated Algorithm Configuration and Design by Leslie Pérez Cáceres, Thomas Stützle and Manuel López-Ibáñez
• Evolution of Neural Networks by Risto Miikkulainen
• Genetic improvement: Taking real-world source code and improving it using computational search methods by Sandy Brownlee, Saemundur Haraldsson, and John R. Woodward

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• Keep Learning - Keep Learning: Towards optimisers that continually improve and/or adapt
• GEWS2023 - Grammatical Evolution Workshop - 25 years of GE
• NEWK - Neuroevolution at work

Workshop Keynote

Speaker: Rong Qu Title: A General Model and Framework for Automated Algorithm Design Abstract: Extensive efforts have been made to design evolutionary algorithms for solving combinatorial optimisation problems (COPs) in real-world applications across industry and business sectors. The rich knowledge of algorithm design is, however, mostly discarded or retained locally with experts of different experience. This seminar presents our recent research establishing a general framework based on a general model for algorithm design. With the general framework, evolutionary algorithms and machine learning, etc. can be used to automatically design new algorithms, knowledge retained can also sustain knowledge sharing, reusing, and potentially interpretability. Short Bio: Dr. Rong Qu is an Associate Professor at the COL Lab, School of Computer Science, University of Nottingham. Her main research interests include the modelling and optimisation with evolutionary algorithms and machine learning in operational research and artificial intelligence, and hybridisations of these techniques for combinatorial optimisation. Her research leads to more than 10k citations at Google Scholar (h-index 44). Dr. Qu is an Associate Editor at six leading journals including EAAI, IEEE TEVC, IEEE CIM and JORS, etc. She is an IEEE Senior Member since 2012, and has been the Chair / vice-chair of Task Committee of Intelligence Systems and Applications and several Task Forces at the IEEE Computational Intelligence Society. Dr. Qu holds a 2022 Leverhulme Trust Senior Research Fellowship at The Royal Society.

Accepted Papers

• Weiyao Meng, Rong Qu. “Sequential Rule Mining for Automated Design of Meta-heuristics”
• Vladimir Stanovov, Eugene Semenkin. “Genetic Programming for Automatic Design of Parameter Adaptation in Dual-Population Differential Evolution”
• Gabriel Kopito, Jonathan Schwartz, Julien Amblard, Robert Filman, Landon Rabern. “MLStar: A System for Synthesis of Machine-Learning Programs”
• Darren Chitty, James Lewis, Ed Keedwell. “Using a Parallel Ensemble of Sequence-Based Selection Hyper-Heuristics for Electric Bus Scheduling”
• Yunshuang Xiao, Leslie Pérez Cáceres, Manuel López-Ibáñez, Thomas Stützle. “Algorithm Configuration via Continuously Racing: Preliminary Results”

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 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 Genetic Programming has most famously been used to this end, other evolutionary algorithms 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 evolutionary algorithms 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 evolutionary algorithms for evolving classification models in data mining and machine learning, a genetic programming 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 is operating 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 evolutionary algorithm. In contrast to standard Genetic Programming, which attempts to build programs from scratch from a typically small set of atomic functions, generative 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 in any way 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, Genetic Programming was used to evolve mate selection in evolutionary algorithms [11]; in 2011, Linear Genetic Programming was used to evolve crossover operators [12]; more recently, Genetic Programming 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 Evolutionary Algorithm 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 Genetic Programming [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].

Call for Papers

• Applications of hyper-heuristics, including general-purpose automatic algorithm configuration methods for the design of metaheuristics, in particular evolutionary algorithms, and other algorithms for application domains such as optimization, data mining, machine learning, image processing, engineering, cyber security, critical infrastructure protection, and bioinformatics.
• Novel hyper-heuristics, including but not limited to genetic programming based approaches, automatic configuration methods, and online, offline and life-long hyper-heuristics, with the stated goal of designing or improving the design of algorithms.
• Empirical comparison of hyper-heuristics.
• Theoretical analyses of hyper-heuristics.
• Studies on primitives (algorithmic components) that may be used by hyper-heuristics as the search space when automatically designing algorithms.
• Automatic selection/creation of algorithm primitives as a preprocessing step for the use of hyper-heuristics.
• Analysis of the trade-off between generality and effectiveness of different hyper-heuristics or of algorithms produced by a hyper-heuristic.
• Analysis of the most effective representations for hyper-heuristics (e.g., Koza style Genetic Programming versus Cartesian Genetic Programming).
• Asynchronous parallel evolution of hyper-heuristics.

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 2023 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 2023 Conference Companion Proceedings. By submitting a paper, the author(s) agree that, if their paper is accepted, they will:

• Submit a final, revised, camera-ready version to the publisher on or before the camera-ready deadline.
• Register at least one author by May 10, 2023 to participate in the conference.
• Provide a pre-recorded version of the talk and be present during the assigned slot to present the work and/or answer questions from the audience.

Co-Chairs (in alphabetical order)

	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 is Editor-in-Chief of Evolutionary Computation (MIT Press) from January 2016 and an elected member and treasurer 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.
	E-mail: dtauritz@acm.org Daniel R. Tauritz is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University (AU), Director for National Laboratory Relationships in the Samuel Ginn College of Engineering, Interim Director and Chief Cyber AI Strategist of the Auburn Cyber Research Center, the founding Head of AU's Biomimetic Artificial Intelligence Research Group (BioAI Group), a cyber consultant for Sandia National Laboratories, a Guest Scientist at Los Alamos National Laboratory (LANL), and founding academic director of the LANL/AU Cyber Security Sciences Institute (CSSI). He received his Ph.D. in 2002 from Leiden University. He served previously as GECCO 2010 Late Breaking Papers Chair, GECCO 2012 & 2013 GA Track Co-Chair, and every year since 2015 as co-chair of the ECADA workshop and co-instructor of the hyper-heuristics tutorial, both at GECCO. For several years he has served on the GECCO GA track program committee, the IEEE Congress on Evolutionary Computation program committee, and a variety of other international conference program committees. His research interests include the design of generative hyper-heuristics and self-configuring evolutionary algorithms and the application of computational intelligence techniques in cyber security, critical infrastructure protection, and program understanding. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.
	E-mail: j.woodward@lboro.ac.uk John R. Woodward is head of Computer Science at the University of Loughborough. Previously he was a senior lecturer and head of The Operational Research Group at the Queen Mary University of London. Formerly he was a lecturer at the University of Stirling, within the CHORDS group and was employed on the DAASE project. Before that he was a lecturer for four years at the University of Nottingham. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, particularly Search Based Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming. He has over 50 publications in Computer Science, Operations Research and Engineering which include both theoretical and empirical contributions, and given over 50 talks at international conferences and as an invited speaker at universities. He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities.

Previous ECADA workshops

• 12th ECADA Workshop @GECCO 2022 - Boston, U.S.A.
• 11th ECADA Workshop @GECCO 2021 - Electronic-only conference
• 10th ECADA Workshop @GECCO 2020 - Electronic-only conference
• 9th ECADA Workshop @GECCO 2019 - Prague, Czech Republic
• 8th ECADA Workshop @GECCO 2018 - Kyoto, Japan
• 7th ECADA Workshop @GECCO 2017 - Berlin, Germany
• 6th ECADA Workshop @GECCO 2016 - Denver, Colorado
• 5rd ECADA Workshop @GECCO 2015 - Madrid, Spain
• 4th ECADA Workshop @GECCO 2014 - Vancouver, BC, Canada
• 3rd ECADA Workshop @GECCO 2013 - Amsterdam, The Netherlands
• 2nd ECADA Workshop @GECCO 2012 - Philadelphia, PA, USA
• 1st ECADA Worlkshop @GECCO 2011 - Dublin, Ireland