An improved genetic programming algorithm based on bloat control
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Zhang:2023:ICBASE,
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author = "Wei Zhang and De-Liang Hua and Si-Hai Li and
Zhen Ren and Zhi-Ling Yu",
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booktitle = "2023 4th International Conference on Big Data \&
Artificial Intelligence \& Software Engineering
(ICBASE)",
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title = "An improved genetic programming algorithm based on
bloat control",
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year = "2023",
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pages = "406--413",
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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.",
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keywords = "genetic algorithms, genetic programming, Training,
Software algorithms, Sociology, Search problems,
Prediction algorithms, Complexity theory, Bloat
control, Selection strategy, Penalty fitness, Density",
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DOI = "doi:10.1109/ICBASE59196.2023.10303075",
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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