- author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang",
- title = "Correlation Coefficient based Recombinative Guidance for Genetic Programming Hyper-heuristics in Dynamic Flexible Job Shop Scheduling",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2021",
- volume = "25",
- number = "3",
- pages = "552--566",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Recombinative Guidance, Correlation Coefficient, Hyper-heuristics, Dynamic Flexible Job Shop Scheduling",
- ISSN = "1089-778X",
-
DOI = "
doi:10.1109/TEVC.2021.3056143",
- abstract = "Dynamic flexible job shop scheduling is a challenging combinatorial optimisation problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic environments. Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for dynamic flexible job shop scheduling. However, in traditional genetic programming, recombination between parents may disrupt the beneficial building-blocks by choosing the crossover points randomly. This paper proposes a recombinative mechanism to provide guidance for genetic programming to realise effective and adaptive recombination for parents to produce offspring. Specifically, we define a novel measure for the importance of each subtree of an individual, and the importance information is used to decide the crossover points. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building-blocks of one parent and incorporating good building-blocks from the other. The proposed algorithm is examined on six scenarios with different configurations. The results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms on most tested scenarios, in terms of both final test performance and convergence speed. In addition, the rules obtained by the proposed algorithm have good interpretability.",
-
notes = "Evolutionary Computation Research Group, School of
Engineering and Computer Science, Victoria University
of Wellington, Wellington 6140, New Zealand.
Also known as \cite{9344816}",
