Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Zhang:2020:EuroGP,

• author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang",
• title = "Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling",
• booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming",
• year = "2020",
• month = "15-17 " # apr,
• editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
• series = "LNCS",
• volume = "12101",
• publisher = "Springer Verlag",
• address = "Seville, Spain",
• pages = "262--278",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Guided subtree selection, JSS, Scheduling heuristic, Dynamic flexible job shop scheduling",
• isbn13 = "978-3-030-44093-0",
• video_url = "https://www.youtube.com/watch?v=qf7hzHmxuAE",
• DOI = "doi:10.1007/978-3-030-44094-7_17",
• abstract = "Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.",
• notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020",

}

Genetic Programming entries for Fangfang Zhang Yi Mei Su Nguyen Mengjie Zhang

Citations