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The Special Session on Evolutionary Machine Learning (EML) of Evo Apps 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).
Giving 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.
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 deadline: 1 November 2019
Evo*: 15-17 April 2020
Submissions must be original and not published elsewhere. They will be peer reviewed by members of the program committee. The reviewing process will be double-blind, so please omit information about the authors in the submitted paper. Submit your
manuscript in Springer LNCS format and provide up to five keywords in your Abstract.