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

Accepted papers will be published by Springer Nature in the Lecture Notes in Computer Science series. Submissions must be original and not published elsewhere. They will be peer reviewed by at least three members of the program committee. The reviewing process will be double-blind, so please omit information about the authors in the submitted paper.

As a joint EuroGP+EvoAPPS track, authors should decide whether their paper will be treated within EvoApplications or EuroGP by selecting the corresponding submission link below. Both conferences are ranked CORE B.

Follow these instructions to submit a paper.

Organizers

• Penousal Machado
  University of Coimbra, Portugal
  machado(at)dei.uc.pt
• Mengjie Zhang
  Victoria University of Wellington, New Zeland
  Mengjie.Zhang(at)ecs.vuw.ac.nz