This joint track on Evolutionary Machine Learning (EML) will provide a specialized forum of discussion and exchange of information for researchers interested in exploring approaches that combine nature and nurture, with the long-term goal of evolving Artificial Intelligence (AI).

In response to the growing interest in the area, and consequent advances of the state-of-the-art, the special session covers theoretical and practical advances on the combination of Evolutionary Computation (EC) and Machine Learning (ML) techniques.

Download the CFP in PDF here.

Topics of Interest

Topics of interest include, but are not limited to:

  • EC as an ML technique: Using EC to solve typical ML tasks such as Classification or Clustering
  • EC applied ML algorithms: Neuroevolution, Feature Selection, Feature Engineering, Evolutionary Adversarial Models
  • ML applied to EC: Surrogate-model design by ML for EC, Learning Problem Structure, ML for Diversity, Designing Search Strategies, Predicting Promising Regions, Using ML to Decrease Computational Effort
  • Real world applications issues: EC for Fairness, Robustness, Trustworthiness and Explainability; Green EML
  • Emerging topics: EC for AutoML; EC for Transfer Learning; EC for Multitasking; Evolving Learning Functions, Neurons and Linkage; EC for Verification and Validation of ML

Submission Details

Submission closed.


  • Penousal Machado
    University of Coimbra, Portugal
  • Mengjie Zhang
    Victoria University of Wellington, New Zeland