Zhixing Huang Wins the Best Paper Award at EuroGP 2022

Congratulations to Zhixing Huang for winning the Best Paper Award at the European Conference on Genetic Programming (EuroGP) 2022 for their paper “An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling”.

Abstract

Dynamic job shop scheduling has a wide range of applications in reality such as order picking in warehouse. Using genetic programming to design scheduling heuristics for dynamic job shop scheduling problems becomes increasingly common. In recent years, multitask genetic programming-based hyper-heuristic methods have been developed to solve similar dynamic scheduling problem scenarios simultaneously. However, all of the existing studies focus on the tree-based genetic programming. In this paper, we investigate the use of linear genetic programming, which has some advantages over tree-based genetic programming in designing multitask methods, such as building block reusing. Specifically, this paper makes a preliminary investigation on several issues of multitask linear genetic programming. The experiments show that the linear genetic programming within multitask frameworks have a significantly better performance than solving tasks separately, by sharing useful building blocks.

Yi Mei
Yi Mei
Group Coordinator, Associate Professor

My research interests include evolutionary computation and machine learning for combinatorial optimisation, genetic programming, hyper-heuristics, and explainable AI.