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ECOM - Evolutionary Combinatorial Optimization and Metaheuristics

Description

The ECOM track aims to provide a forum for presenting and discussing high-quality research on metaheuristics for combinatorial optimisation problems. Works published at ECOM frequently include improved optimisers for combinatorial problems, e.g., jobshop scheduling, MAX3SAT, TSP, and other benchmark and real-world problems. This track encourages works that focus on enhancing our understanding of combinatorial problems and/or the algorithms used to address them. Therefore, papers concerning such topics, for example, landscape analysis, Walsh decomposition, or algorithm behaviour analysis are welcome. The particular interest is in using this knowledge to propose more effective and/or efficient optimisers. Thus, works referring to the grey-box setting are welcome.

While ECOM traditionally emphasizes well-established benchmark problems to support rigorous comparison and reproducibility, we also welcome novel real-world problem formulations or instances, provided they are clearly described and reproducible and benchmarks are also considered. For this track we particularly emphasise high-quality reproducible science. Therefore, we expect all submissions to contain a link to an (anonymised) data/code repository in the initial (pre-camera ready) submission.

Scope

The ECOM track encourages original submissions on the application of evolutionary algorithms and metaheuristics to combinational optimization problems. The topics for ECOM include, but are not limited to:

  • Representation techniques
  • Neighborhoods and efficient algorithms for searching them
  • Variation operators for stochastic search methods
  • Search space and landscape analysis
  • Comparisons between different techniques (including exact methods)
  • Constraint-handling techniques
  • Automated design of combinatorial optimisation algorithms
  • Characteristics of problems and problem instances


Notice that the submission of very narrowed case studies of real-life problems as well as highly specific theoretical results on the performance of evolutionary algorithms may be better suited to other tracks at GECCO.

Track Chairs

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Sarah L. Thomson

Edinburgh Napier University

Sarah L. Thomson is a Lecturer at Edinburgh Napier University in Scotland, United Kingdom. She has held this position since 2023, having previously been at the University of Stirling. Her research primarily focuses on fitness landscape analysis in evolutionary computation for combinatorial optimisation and she has published extensively on this topic. She completed her PhD in 2020 under the supervision of Professor Gabriela Ochoa. Recently, Dr Thomson has been looking at the notion of explainability for search algorithms more broadly and is particularly interested in the intersection of explainable AI and evolutionary computing. Dr Thomson has led the organisation of the landscape workshop at GECCO in 2023, 2024, and 2025, as well as serving as ECOM track co-chair in 2025. She has also served as publication chair for EvoApps 2025 and will serve as workshop chair for PPSN 2026.

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Niki van Stein

Leiden University, Netherlands

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.