Workshop on Advances in Genetic Programming and Evolutionary Optimization

#computational-intelligence #evolutionary-computation #genetic-programming
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This workshop explores cutting-edge methodologies and applications in Evolutionary Computation. Addressing the growing demand for explainable and efficient AI, the session highlights recent breakthroughs in genetic programming (GP) and advanced evolutionary algorithms.

Key topics include fundamental evolutionary learning via GP, advances in implicit symbolic regression, and novel multi-objective evolution strategies for ill-conditioned problems. Furthermore, the workshop tackles practical deployment challenges, featuring discussions on lightweight surrogate-assisted methods and the application of GP for explainable urban traffic signal control.


  Date and Time

  Location

  Hosts

  Registration


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  • South China University of Technology
  • Panyu District, Guangzhou Higher Education Mega Center
  • Guangzhou, Guangdong
  • China 510000
  • Building: B3
  • Room Number: 308
  • Contact Event Host
  • Starts 12 June 2026 04:30 PM UTC
  • Ends 14 June 2026 04:30 PM UTC
  • No Admission Charge

  Speakers

zhixing of South China University of Technology

Topic:

Evolutionary learning by genetic programming

Weili of Guangdong Polytechnic Normal University

Topic:

Towards explainable traffic signal control for urban networks through genetic programming


Chengyu of City University of Hong Kong

Topic:

4. Multi-objective Evolution Strategies for Dealing with Non-separable and Ill-conditioned Multi-objective Problems

Qite of Guangdong University of Technology

Topic:

A Study on Lightweight Surrogate Methods in Surrogate-Assisted Evolutionary Algorithms


Junlan

Topic:

Advances in Implicit Symbolic Regression




Agenda

Theme: Advances in Genetic Programming and Evolutionary Optimization

Time: 15:00 – 17:30 (2 hours 30 minutes)

Time Speaker Presentation Title
15:00 - 15:30 Zhixing Huang Evolutionary Learning by Genetic Programming
15:30 - 16:00 Weili Liu Towards Explainable Traffic Signal Control for Urban Networks Through Genetic Programming
16:00 - 16:30 Chengyu Lu Multi-objective Evolution Strategies for Dealing with Non-separable and Ill-conditioned Multi-objective Problems
16:30 - 17:00 Qite Yang A Study on Lightweight Surrogate Methods in Surrogate-Assisted Evolutionary Algorithms
17:00 - 17:30 Junlan Dong Advances in Implicit Symbolic Regression