Evolutionary Computation and Decision Making
Webpage: https://sites.exeter.ac.uk/ecmcdm/
Description
Solving real-world optimisation problems typically involve an expert or decision-maker. Decision making (DM) tools have been found to be useful in several such applications e.g., health care, education, environment, transportation, business, and production. In recent years, there has also been growing interest in merging Evolutionary Computation (EC) and DM techniques for several applications. This has raised amongst others the need to account for explainability, fairness, ethics and privacy aspects in optimisation and DM. This workshop will showcase research that is at the interface of EC and DM.
The workshop on Evolutionary Computation and Decision Making (EC + DM) to be held in GECCO 2025 aims to promote research on theory and applications in the field. Topics of interest include:
• Interactive multiobjective optimisation or decision-maker in the loop
• Visualisation to support DM in EC
• Aggregation/trade-off operators & algorithms to integrate decision maker preferences
• Fuzzy logic-based DM techniques
• Bayesian and other DM techniques
• Interactive multiobjective optimisation for (computationally) expensive problems
• Using surrogates (or metamodels) in DM
• Hybridisation of EC and DM
• Scalability in EC and DM
• DM and machine learning
• DM in a big data context
• DM in real-world applications
• Use of psychological tools to aid the decision-maker
• Fairness, ethics and societal considerations in EC and DM
• Explainability in EC and DM
• Accounting for trust and security in EC and DM
Organizers
Dr Tinkle Chugh is a Lecturer in Computer Science at the University of Exeter. He is the Associate Editor of the Complex and Intelligent Systems journal. Between Feb 2018 and June 2020, he worked as a Postdoctoral Research Fellow in the BIG data methods for improving windstorm FOOTprint prediction project funded by Natural Environment Research Council UK. He obtained his PhD degree in Mathematical Information Technology in 2017 from the University of Jyväskylä, Finland. His thesis was a part of the Decision Support for Complex Multiobjective Optimization Problems project, where he collaborated with Finland Distinguished Professor (FiDiPro) Yaochu Jin from the University of Surrey, UK. His research interests are machine learning, data-driven optimization, evolutionary computation, and decision-making.
Richard is Professor of Applied Artificial Intelligence, and Associate Dean for Business Engagement, Civic & Cultural Partnerships in the Faculty of Humanities, The University of Manchester, UK. He is also an Editorial Board Member of several international journals, an Alan Turing Fellow Alumni, has served in numerous chair roles for different AI conferences, and is a Senior Scientist at Eharo, and AI Advisor for River Capital (private equity), Ark Biotech (bioprocessing), and GuruAI (music education). Currently, Richard is also creating a UoM software spinout focussed on identifying and repairing security vulnerabilities in source code. Prior to Manchester, he was Honorary Lecturer and Research Associate at the Biochemical Engineering Department, University College London. He studied Business Engineering at the Karlsruhe Institute of Technology and the Royal Melbourne Institute of Technology and completed a PhD in Computer Science (Machine Learning & Optimization) at The University of Manchester. Richard's research interests are in the development and application of sequential decision-making methods to problems with multiple objectives, uncertainties and resourcing issues arising in areas such as healthcare, manufacturing, engineering, music, sports, and finance. Richard has attracted a total of £45M+ in grant funding as PI/co- I from UKRI, industry, and other sources, and led the development of several commercially available AI tools.
Ana B. Ruiz is a Senior Lecturer in the area of Quantitative Methods for Economy at the Department of Applied Economics (Mathematics), at the University of Málaga (Spain). She holds a PhD in Mathematics (2012) from the University of Málaga, where she also received her BSc degree in Mathematics (2006). Her research is focused on multi-objective optimization and multiple-criteria decision-making approaches, such as evolutionary algorithms, interactive methods, and reference point-based techniques, and their applications to decision-making processes arising in different fields, such as education, portfolio, sustainability, or engineering. She has participated in more than 17 research projects financed by international, national and regional institutions, and she has collaborations with several international researchers. Currently, she is one of the main researchers of a partner in a European Project granted for the development of safe and sustainable by design coatings for several industrial sectors. In addition, Ana B. Ruiz teaches graduate courses in Economics, Business Administration, and Marketing, in the Master's Course in Quantitative Methods for Economy, and the PhD program in Economy and Business Administration.