Instance Rotation Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Fangfang_Zhang:ieeeTEC4,

• author = "Fangfang Zhang and Yi Mei and Su Nguyen and Kay Chen Tan and Mengjie Zhang",
• title = "Instance Rotation Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job Shop Scheduling",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2023",
• volume = "27",
• number = "5",
• pages = "1192--1206",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Brood recombination, dynamic job-shop scheduling, JSS, instance rotation, surrogate",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2022.3180693",
• size = "15 pages",
• abstract = "Genetic programming has achieved great success for learning scheduling heuristics in dynamic job shop scheduling. In theory, generating a large number of offspring for genetic programming, known as brood recombination, can improve its heuristic generation ability. However, it is time-consuming to evaluate extra individuals. Phenotypic characterisation based surrogates with K-nearest neighbours have been successfully used for genetic programming to preselect only promising individuals for real fitness evaluations in dynamic job shop scheduling. However, sample individuals used by surrogate are from only the current generation, since the fitness of individuals across generations are not comparable due to the rotation of training instances. The surrogate cannot accurately estimate the fitness of an offspring that is far away from all the limited sample individuals at the current generation. This paper proposes an effective instance rotation based surrogate to address the abo",
• notes = "also known as \cite{9789507}",

}

Genetic Programming entries for Fangfang Zhang Yi Mei Su Nguyen Kay Chen Tan Mengjie Zhang

Citations