L4EC - Learning for Evolutionary Computation
Description
Learning for Evolutionary Computation (L4EC) track recognises high-quality research that leverages machine learning (including deep learning and reinforcement learning) or statistical techniques and concepts to improve heuristics and algorithmic components in the field of evolutionary computation (EC).
If your work covers advances in the theory and application of using evolutionary computation methods to solve Machine Learning (ML) problems, you should consider the Evolutionary Machine Learning (EML) track.
Scope
Topics of interest include, but are not limited to:
- Methods for automated algorithm design, selection, and configuration,
- Mechanisms that learn how to control or coordinate a set of EC algorithms, such as parameter tuning, parameter control, dynamic algorithm selection/configuration, and meta-heuristics,
- EC algorithms integrating methods to extract knowledge from the population dynamics, search trajectory and/or the genotype,
- Surrogate-based or surrogate-assisted optimization of expensive fitness functions, including multi-fidelity approaches,
- Feature-based methods that learn to characterize optimization problems, such as exploratory landscape analysis (ELA) and fitness landscape analysis.
Track Chairs
Anna V Kononova
LIACS, Leiden University, Netherlands
Anna V. Kononovais an Assistant Professor at the Leiden Institute of Advanced ComputerScience. She received her MSc degree in Applied Mathematics from Yaroslavl State University (Russia) in 2004 and PhD degree in Computer Science from University of Leeds (UK) in 2010. After a total of 5 years of postdoctoral experiences at Technical University Eindhoven (The Netherlands) and Heriot-Watt University (Edinburgh, UK), Anna has spent a number of years working as a mathematician in industry. Her current research interests include analysis of optimisation algorithms and machine learning.Mario Andrés Muñoz
School of Computing and Information Systems, The University of Melbourne, Australia. | webpage
Mario Andrés Muñoz is a Senior Research Fellow at the School of Computing and Information Systems, The University of Melbourne, and the ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications (OPTIMA). He received the B.Eng. and M.Eng. degrees in Electronics Engineering from Universidad del Valle, Colombia, in 2005 and 2008, respectively, and the Ph.D. degree in Engineering from The University of Melbourne, Australia, in 2014. His research interests focus on applying optimisation, computational intelligence, signal processing, data analysis, and machine learning methods to ill-defined science, engineering and medicine problems.