Webinar Speaker: Professor Qi Chen
Webinar Chair: Bing Xue
Date and Time: 11th June 2018 at 09:00 BST. This is 8am GMT time (due to British summer time).
Abstract: Genetic Programming (GP) based symbolic regression, as a kind of regression analysis, is to find the relationship between the input data and the output data and express this relationship in a mathematical model for some given data of the unknown process. GP based symbolic regression provides a way to getting a good insight into the data generating systems. It is extremely useful when we do not have any domain knowledge of the data generating process. At the same time, by not requiring any specific model and letting the patterns in the data itself reveal the appropriate models, GP based symbolic regression is not affected by human bias. It is clear that the importance of GP based symbolic regression will increase as the complexity of the solved problems are increasing in science and industry. In this webinar, we will discuss the background and basic mechanism of GP based symbolic regression, and enhancements that have improved symbolic regression.
Registration link: https://attendee.gotowebinar.com/register/8540749429968458754
Webinar ID: 208-996-579
Qi Chen received the B.E. degree in automation from the University of South China, Hunan, China, in 2005, the M.E. degree in software engineering from the Beijing Institute of Technology, Beijing, China, in 200, and the Ph.D. degree in Computer Science from Victoria University of Wellington (VUW), Wellington, New Zealand. Since 2014, she has been with the Evolutionary Computation Research Group, VUW. Her current research interests include genetic programming for symbolic regression, machine learning, evolutionary computation, feature selection, feature construction, transfer learning, domain adaptation, and statistical learning theory. Ms Chen serves as a Reviewer of international conferences, including the IEEE Congress on Evolutionary Computation, and international journals, including the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, Knowledge-based Systems and the Journal of Heuristics.