An improved genetic programming algorithm based on bloat control

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

@InProceedings{Zhang:2023:ICBASE,

• author = "Wei Zhang and De-Liang Hua and Si-Hai Li and Zhen Ren and Zhi-Ling Yu",
• booktitle = "2023 4th International Conference on Big Data \& Artificial Intelligence \& Software Engineering (ICBASE)",
• title = "An improved genetic programming algorithm based on bloat control",
• year = "2023",
• pages = "406--413",
• abstract = "Aiming at the disadvantages of long training time and high model complexity caused by individual bloat in genetic programming, an improved genetic programming algorithm based on bloat control is proposed. First, species are divided according to the differences between individuals, and individuals are evaluated by adjusted penalty fitness. Then, during the population evolution stage, individuals are selected by density and adjusted penalty fitness, and the improved search strategy is used to search and optimise the selected individuals. The survival mechanism of individuals retains the excellent evolutionary information in the population. Finally, the 7 benchmark functions are simulated and compared with other relevant bloat control algorithms. The experimental results show that the proposed algorithm compared with the 4 comparative algorithms can effectively control individual bloat on the basis of ensuring the optimisation ability.",
• keywords = "genetic algorithms, genetic programming, Training, Software algorithms, Sociology, Search problems, Prediction algorithms, Complexity theory, Bloat control, Selection strategy, Penalty fitness, Density",
• DOI = "doi:10.1109/ICBASE59196.2023.10303075",
• month = aug,
• notes = "Also known as \cite{10303075}",

}

Genetic Programming entries for Wei Zhang De-Liang Hua Si-Hai Li Zhen Ren Zhi-Ling Yu

Citations