Explainable models for predicting crab weight based on genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Shi:2025:ecoinf,

• author = "Tao Shi and Lingcheng Meng and Limiao Deng and Juan Li",
• title = "Explainable models for predicting crab weight based on genetic programming",
• journal = "Ecological Informatics",
• year = "2025",
• volume = "88",
• pages = "103131",
• keywords = "genetic algorithms, genetic programming, Weight prediction, Crab, Symbolic regression, Explainable artificial intelligence",
• ISSN = "1574-9541",
• URL = "https://www.sciencedirect.com/science/article/pii/S1574954125001402",
• DOI = "doi:10.1016/j.ecoinf.2025.103131",
• abstract = "Reliable weight prediction plays an important role in commercial transactions and population management of crabs. Existing research usually used predefined models to explain the relationship between the weight and the length of crabs. In this paper, we propose an effective regression method using genetic programming (GP) to build explainable models, which include more features to explore potential relationships between the weight and the physical features of crabs. The GP-based method has been evaluated on a publicly available dataset of crabs. The experimental results were compared with several baseline methods for predicting two kinds of crab weights. GP shows the best performance among all the baseline methods on the test set, i.e., 90.8percent for predicting the weight of crabs and 81.3percent for predicting the shucked weight of crabs in terms of coefficient of determination. Thanks to the explicit ability of feature selection, GP can select more important features to improve the prediction performance. More importantly, the generated models can provide potential interpretability, which is particularly valuable for domain experts in fisheries management and ecological research",

}

Genetic Programming entries for Tao Shi Lingcheng Meng Limiao Deng Juan Li

Citations