Call for Papers: CEC 2025 Special Session on “Learning-aided Evolutionary Computation: Principles, Algorithms, and Applications”, June 8 - 12, 2025, Hangzhou, China
Summary of the Special Session
Evolutionary computation (EC) algorithms are promising in solving optimization problems as they do not require strict mathematical problem characteristics and can find the global or near-global optimum within a reasonable timeframe. Over the years, numerous research efforts have focused on developing advanced EC algorithms, particularly on designing efficient evolutionary mechanisms, including new genetic operators, reliable selection criteria, and intensive local search heuristics. However, relying solely on evolutionary methods to solve the problem may be still inefficient. Recently, researchers have incorporated knowledge to enhance EC’s efficiency in approaching the global optimum. This paradigm is known as Learning-aided EC (LEC).
LEC, which learns knowledge from the problem, solution, and evolution, has demonstrated superiority in enhancing population reproduction and optimization efficiency. Indeed, a substantial amount of knowledge has been applied in the EC community in recent years. However, the field is still in its infancy, and there is significant potential for designing novel, efficient, and effective LEC methods to achieve better convergence and diversity.
This special session aims to bring together researchers investigating the principles, algorithms, and applications of LEC. Specifically, the session focuses on methods for effective and efficient knowledge representation, knowledge management, search mechanisms, and learning techniques in LEC. Authors are invited to submit their original research to this special session.
Scope and Topics
Learning knowledge from problems, from solutions, and/or from evolution process
Multi-view knowledge learning for LEC
Learning restricted knowledge for LEC
LEC framework for distributed computing
Efficient knowledge representation in LEC
Efficient learning model for LEC
Automatic parameter of LEC learning system
LEC for complex problems (e.g., multitask problems and multimodal problems)
Knowledge acquisition from data and reuse for evolutionary optimization
Large language model, surrogate models/or other learning methods for aiding EC
Balance in learning and evolution
Real-world applications of LEC, e.g. neural architecture search, federated learning, vehicle design, orbit optimization, circuit design, drug molecular design, portfolio optimization etc.
Important Dates
15 January 2025:Paper Submission Deadline 15 March 2025: Paper Acceptance Notification 1 May 2025: Final Paper Submission & Early Registration Deadline 8-12 June 2025: Conference Date
Submissions
Organizers
Dr. Jian-Yu Li, Nankai University Xin-Xin Xu, Ocean University of China Dr. Yi Jiang, Hanyang University Professor Zhi-Hui Zhan，Nankai University
