Genetic Programming for Symbolic Regression: A Study on Fish Weight Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Yunhan_Yang:2021:CEC,
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author = "Yunhan Yang and Bing Xue and Linley Jesson and
Mengjie Zhang",
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title = "Genetic Programming for Symbolic Regression: A Study
on Fish Weight Prediction",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2021",
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pages = "588--595",
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abstract = "The fish weight is a very important factor in
fisheries science and management since it explains the
growth and living conditions of fish populations. A
power regression model has been commonly used to
explain the relationship between the fish length and
the weight. In this work, Genetic Programming (GP) for
symbolic regression is used to build a new model for
predicting the fish weight, which allows us to include
more features into the model to discover any hidden
relationship, and the GP based symbolic regression
makes the model interpretable comparing with other
machine learning methods. A publicly available dataset
is taken with four species of fish which includes more
features than just the fish length that is commonly
used in existing models. The proposed GP based symbolic
regression method has been examined on those four
species. The results are compared with the weight
prediction baseline methods including Linear
Regression, Power Regression model, k-Nearest
Neighbour, Ridge Regression, Decision Tree, Random
Forest, Gradient Boosting, and Multilayer Perceptron.
GP performs better, or at least as good as the baseline
methods on the test set. Furthermore, the generated GP
models also can select different features for different
species to improve the prediction performance due to
GP's explicit feature selection ability. Some models
are interpretable with relatively simple expression.
The GP method is also able to find models that are
similar to the power regression model, but more
features are included rather than a single length
feature to gain improved prediction performance.",
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keywords = "genetic algorithms, genetic programming, Computational
modelling, Sociology, Linear regression, Predictive
models, Multilayer perceptrons, Fish",
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DOI = "doi:10.1109/CEC45853.2021.9504963",
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month = jun,
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notes = "Also known as \cite{9504963}",
- }
Genetic Programming entries for
Yunhan Yang
Bing Xue
Linley Jesson
Mengjie Zhang
Citations