EML - Evolutionary Machine Learning

Description

The Evolutionary Machine Learning (EML) track at GECCO covers advances in the theory and application of using evolutionary computation methods to solve Machine Learning (ML) problems. Evolutionary methods can tackle many different tasks within the ML context, including problems related to supervised, unsupervised, semi-supervised, and reinforcement learning, as well as more recent topics such as transfer learning and domain adaptation, deep learning, representation learning, interpretability of machine learning models, and learning with unbalanced data and missing data.

The global search capability featured by evolutionary methods provides a valuable complement to the local search process that typically underpins non-evolutionary ML methods, and combinations of the two often demonstrate desirable promise in practice.

We encourage submissions related to theoretical advances, innovation of new algorithms, and renovation/improvement of existing algorithms, as well as application-focused papers. Authors are strongly encouraged to compare their EML approaches to the corresponding state-of-the-art non-evolutionary ML methods, where appropriate.

If your work focuses on the use of ML for solving evolutionary computation problems, please consider the new L4EC track that complements this track.

Scope

More concretely, topics of interest include but are not limited to:

• Theoretical and methodological advances on EML
• Evolutionary ensemble learning
• Evolutionary transfer learning and domain adaptation
• Evolutionary transfer learning and domain adaptation
• Evolutionary representation learning
• Learning Classifier Systems (LCS) and evolutionary rule-based systems
• Evolutionary computation techniques (e.g. genetic programming, particle swarm optimisation, and differential evolution) for solving ML tasks such as clustering, dimension reduction (feature selection, extraction, and construction), and representation learning
• AutoML (e.g. hyper-parameter tuning for ML) via evolutionary methods
• EML with a small number of examples, unbalanced data or missing data
• Visualizing or improving the interpretability of ML models via evolutionary approaches
• Parallel, distributed, and decentralized EML, including approaches based on high performance computing (with GPUs/TPUs), cloud computing ,and edge computing as well as federated learning
• Applications of EML (non-exhaustive list):
  • Computer vision and image processing
  • Pattern recognition and data mining
  • Bioinformatics, life sciences, medicine, and health
  • Space technology
  • Cognitive systems and modelling
  • Economic modelling
  • Intelligent transportation
  • Cyber security