Chair: Dr. Daniel 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 Hernández Morales daniel.hernandezm @ tectijuana.
Daniel Hernández 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)