EMO 2023


Keynote Speakers

Yaochu Jin is an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, with the Faculty of Technology, Bielefeld University, Germany. He is also a Surrey Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include evolutionary optimization and learning, trustworthy machine learning and optimization, and evolutionary developmental AI.

Prof Jin is presently the Editor-in-Chief of Complex & Intelligent Systems. He is the recipient of the 2018 and 2021 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Web of Science as “a Highly Cited Researcher” consecutively from 2019 to 2021. He is a Member of Academia Europaea and Fellow of IEEE.

Title: Knowledge transfer in Bayesian optimization of heterogeneous multi-objective problems

Abstract: This talk begins with a brief introduction to heterogeneous multi-objective optimization and its application background. Then, some recent ideas of enhancing the efficiency of Bayesian optimization of heterogeneous multi-objective problems by transferring knowledge from computationally cheap objectives to expensive objectives will be presented. We show that knowledge transfer can be achieved by sharing hyperparameters in Gaussian processes and generating synthetic data by either building a co-surrogate between the two objectives or using domain adaptation. Extensive empirical studies verify the effectiveness of various knowledge transfer methods in Bayesian heterogeneous multi-objective optimization.

Heike Trautmann is Professor of Data Science: Statistics and Optimization, both at the Department of Information Systems, University of Münster, Germany and the University of Twente, Netherlands. She is also Director of the European Research Center for Information Systems (ERCIS) and key supporter of the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE). Her research mainly focuses on Data Science, Automated Algorithm Selection and Configuration, Exploratory Landscape Analysis, (Multiobjective) Evolutionary Optimization and Data Stream Mining. She is Associate Editor of the IEEE Transactions on Evolutionary Computation and the Evolutionary Computation Journal (ECJ).

Shared Affiliation:
Data Science: Statistics and Optimization, University of Münster (GE), University of Twente (NL)

Title: Robustness in Trustworthy Artificial Intelligence and Implications on Automated Algorithm Selection
Abstract: Trustworthy Artificial Intelligence (AI) systems are required to be lawful, ethical and robust as stated by the EU high-level expert group on AI in terms of ‘Ethics Guidelines for Trustworthy AI’ in 2019. This is further stressed by the EU project TAILOR (Foundations of Trustworthy AI – integrating, learning, optimisation and reasoning) by means of its ‘Strategic Research and Innovation Roadmap of trustworthy AI’ (2022 – 2030), also emphasizing robustness in the context of the shortterm scientific goal ‘..to enable synergistic collaboration between humans and machines with regards to the criteria of being explainable, safe, robust, fair, (and) accountable’.
While various definitions of AI exist, Machine Learning (ML) and Optimisation are two commonly accepted core AI research areas. This talk will discuss different notions of robustness in ML and optimization, highlighting similarities, differences and context-specific challenges. Moreover, as the notion of robustness has its roots in Statistics, foundations of Robust Statistics will be presented and aligned with ML and Optimisation robustness. Implications on Automated Algorithm Selection as an exemplary AI system are provided together with an inspiring motiviation for trustworthy AI research beyond common EMO and Evolutionary Computation topics

Frank Neumann is a Professor and the leader of the Optimisation and Logistics group at the University of Adelaide and an Honorary Professorial Fellow at the University of Melbourne. His current position is funded by the Australian Research Council through a Future Fellowship and focuses on AI-based optimisation methods for problems with stochastic constraints. Frank has been the general chair of the ACM GECCO 2016 and co-organised ACM FOGA 2013 in Adelaide. He is an Associate Editor of the journals “Evolutionary Computation” (MIT Press) and ACM Transactions on Evolutionary Learning and Optimization. In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of cybersecurity, renewable energy, logistics, and mining.

Title: 20 Years Runtime Analysis of Evolutionary Multi-Objective Optimization – Results and Perspectives
Abstract: Evolutionary multi-objective algorithms have been applied to a wide range of different optimization problems. Understanding these algorithms from a theoretical perspective helps to point out benefits and drawbacks of different evolutionary multi-objective algorithms and design even more efficient approaches. Over the last 20 years, the area of runtime analysis has widely contributed to the theoretical understanding of evolutionary multi-objective optimization for discrete optimization problems. This talk will give an overview on different results obtained during the last 20 years and point out current research gaps and challenges. Topics covered include early results on the optimization of pseudo-Boolean functions, multi-objective combinatorial optimization problems, indicator-based algorithms, Pareto optimization approaches to tackle classical single objective problems, and the impact of diversity mechanisms commonly used in algorithms such as NSGA-II and SPEA2.

Kalyanmoy Deb is the Koenig Endowed Chair Professor at the Department of Elec- trical and Computer Engineering at Michigan State University (MSU), East Lansing, USA. Prior to this position, he was at Indian Institute of Technology Kanpur in India. Prof. Deb’s main research interests are in evolutionary optimization algorithms and their application in optimization and machine learning. He is largely known for his seminal research in Evolutionary Multi-Criterion Optimization. He was awarded the prestigious ‘Infosys Prize’ in 2012, ‘TWAS Prize’ in Engineering Sciences in 2012, ‘CajAstur Mamdani Prize’ in 2011, ‘JC Bose National Fellowship’ in 2011, ‘Distinguished Alumni Award’ from IIT Kharagpur in 2011, ’Edgeworth-Pareto’ award in 2008, Shanti Swarup Bhatnagar Prize in Engineering Sciences in 2005, ‘Thomson Citation Laureate Award’ from Thompson Reuters. His 2002 IEEE-TEC NSGA-II paper is now judged as the Most Highly Cited paper and a Current Classic by Thomson Reuters having more than 4,000+ citations. He is a fellow of IEEE. He has written two text books on optimization and more than 340 international journal and conference research papers. He is in the editorial board on 18 major international journals, including IEEE TEC.

Title: Lessons from 30 Years of EMO: What Lies Ahead

Abstract: Exactly 30 years ago, the evolutionary algorithm (EA) framework was extended to solve two-objective optimization problems. That study and a few others came in a quick succession clearly demonstrated that EA’s population approach provided an ideal platform for finding multiple Pareto-optimal solutions in a single run. Since then, a number of systematic studies and events had taken the simple idea to constitute a field of research and application, largely known as “Evolutionary Multi-criterion Optimization” or EMO. In this lecture, speaker’s views on the key advancements that helped built the EMO field will be presented. Based on the past achievements and current advancements, the speaker will highlight his views on plausible future directions for making the EMO research and application more engaging and rewarding.


Aneta Neumann is a researcher in the School of Computer and Mathematical Sciences at the University of Adelaide, Australia, and focuses on real world problems using evolutionary computation methods. She is also part of the Integrated Mining Consortium at the University of Adelaide. Aneta graduated in Computer Science from the Christian-Albrechts-University of Kiel, Germany, and received her PhD from the University of Adelaide, Australia. She served as the co-chair of the Real-World Applications track at GECCO 2021 and GECCO 2022, and is a co-chair of the Genetic Algorithms track at GECCO 2023. Her main research interests are bio-inspired computation methods, with a particular focus on dynamic and stochastic multi-objective optimization for real-world problems that occur in the mining industry, defence, cybersecurity, creative industries, and public health.

Shared Affiliation:
The University of Adelaide, Australia

Title: Mine Planning under Uncertainty: From Theory to Practice

: Evolutionary algorithms provide great flexibility in dealing with large-scale optimisation problems. Their wide applicability has made evolutionary computing techniques popular optimisation techniques for many real-world applications such as the mining industry. Long-term planning and production scheduling are among the most critical and complex tasks that involve many uncertainties in the area of mining. In this talk, I will discuss the application of evolutionary computation to problems with stochastic constraints as well as how to transfer theoretical results to real-world applications. In particular, I will show how we integrated theoretical findings into mine planning under uncertainty and explain some approaches that have been incorporated into industrial software for mine planning.