Genetic Programming – Introduction, Recent Developments and Applications
This talk has the objective of introducing Genetic Programming (GP), giving a rather practical perspective, with examples of functioning of the algorithm and small simulations. Among the possible different uses of GP, particular focus will be dedicated to GP as a Machine Learning method for the automatic development of predictive models. Under this perspective, a recent and promising variant of GP, called Geometric Semantic GP (GSGP), will be introduced. Then, some GP applications will be discussed, in particular in the area of Drug Discovery. In the final part of the presentation, a recent trend aimed at integrating GSGP with linear scaling will be presented
Leonardo Vanneschi is a Full Professor at NOVA IMS. His main research interests involve Machine Learning, Data Science, Complex Systems, and in particular Evolutionary Computation. His work can be broadly partitioned into theoretical studies on the foundations of Evolutionary Computation, and applicative work. The former covers the study of the principles of functioning of Evolutionary Algorithms, with the final objective of developing strategies to outperform the existing techniques. The latter covers several different fields among which computational biology, image processing, personalized medicine, engineering, logistics, economics and marketing. He has published more than 250 contributions and he has led numerous research projects. He has served as chair in several international scientific conferences and he is a member of the editorial board of the GPEM and the ACM TELO journals. In 2015, he received the Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe.
Berfin Sakallioglu is a Teaching Assistant at NOVA IMS. She obtained her Master in Data Science and Advanced Analytics in March 2023. Her research interests include Data Science, Optimization, Machine Learning, Evolutionary Computation and Genetic Programming.