Chair: Dr. Daniel E. Hernández

Genetic Programming is an evolutionary approach to address learning and automatic program induction problems. It has generated strong results in many domains, and continues to develop as a more mature paradigm in machine learning. On the other hand, Machine Learning has become an extremely popular approach for solving problems following different learning contexts: supervised, unsupervised and semi-supervised.

This special session invites works and talks related to theoretical advances, the development of new algorithms or improvements over existing ones, as well as real-world applications.


Topics of interest include (but are not limited to):

  • Theoretical developments in GP and ML
  • GP performance and behavior
  • Algorithms, representations and operators for GP
  • Search-based software engineering
  • Multi-population GP
  • Multi-objective GP
  • Tree-based, Linear, Graph-based, Grammar-based GP
  • Hybrid models
  • Evolutionary machine learning approaches
  • Ensemble models
  • Reinforcement learning, Transfer learning and Deep learning
  • Interpretability of machine learning models
  • Leaning with unbalanced or missing data
  • Feature extraction, reduction and selection
  • Real-world application


Contact: Dr. Daniel Eduardo E. Hernández Morales  danielhdz.morales @ gmail.com

 

Daniel E Hernandez is a professor at the Tecnológico Nacional de México/ IT de Tijuana, in Tijuana, BC, Mexico. His reasearch interest include several data science and artificial intelligence topics such as: machine learning, feature engineering, evolutionary computation and computer vision. He received his Ph.D. in Computer Science the from Centro de Investigación Científica y de Educación Superior de Ensenada, B.C., (CICESE), México. He is a member of the National Network of Researchers (SNI Level I)