Zhixing Huang, a Victoria University of Wellington PhD student, alongside his co-authors Fangfang Zhang, Yi Mei, and Mengjie Zhang, has recently been awarded the European Conference on Genetic Programming Best Paper Award at the EvoStar 2022 conference. Please join us in congratulating Zhixing and his colleagues for this great achievement! A link to the team’s work is available below.

An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling

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.

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