- author = "Fangfang Zhang and Yi Mei and Su Nguyen and Kay Chen Tan and Mengjie Zhang",
- title = "Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling",
- journal = "IEEE Transactions on Cybernetics",
- year = "2022",
- volume = "52",
- number = "10",
- pages = "10515--10528",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "2168-2267",
-
DOI = "
doi:10.1109/TCYB.2021.3065340",
- abstract = "Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyperheuristic approach based on genetic programming (GP) in this article. Specifically, we introduce the idea in evolutionary multitask learning to GP hyperheuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario.",
-
notes = "also known as \cite{9382963}
Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand",
