Daniel Yska
ABSTRACT
Genetic Programming Hyper-Heuristic (GPHH) has shown success in evolving dispatching rules for dynamic Flexible Job Shop Scheduling (FJSS). In this paper, we focus on feature construction to improve the effectiveness and efficiency of GPHH, and propose a GPHH with Cooperative Co-evolution with Feature Construction (CCGP-FC). The experimental results showed that the proposed CCGP-FC could improve the smoothness of the convergence curve, and thus improve the stability of the evolutionary process. There is a great potential to improve the FC methods, such as filtering the meaningless building blocks, and incorporating with feature selection to improve the terminal set.
- Y. Mei, S. Nguyen, B. Xue, and M. Zhang. 2017. An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming. IEEE Transactions on Emerging Topics in Computational Intelligence 1, 5 (2017), 339--353.Google Scholar
Cross Ref
- Yi Mei, Su Nguyen, and Mengjie Zhang. 2017. Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling. In Asia-Pacific Conference on Simulated Evolution and Learning. Springer, 435--447.Google Scholar
- J. Tay and N. Ho. 2008. Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering 54, 3 (2008), 453--473. Google ScholarDigital Library
- Daniel Yska, Yi Mei, and Mengjie Zhang. 2018. Genetic Programming Hyper-Heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling. In European Conference on Genetic Programming. Springer, 306--321.Google Scholar
Index Terms
- Feature construction in genetic programming hyper-heuristic for dynamic flexible job shop scheduling
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