A Multi-metric Selection Strategy for Evolutionary Symbolic Regression
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- @InProceedings{Zhang:2020:SMC,
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author = "Hu Zhang and Hengzhe Zhang and Aimin Zhou",
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title = "A Multi-metric Selection Strategy for Evolutionary
Symbolic Regression",
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booktitle = "2020 IEEE International Conference on Systems, Man,
and Cybernetics (SMC)",
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year = "2020",
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pages = "585--591",
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abstract = "Evaluation metrics play an important role in accessing
the performance of a regression method. In practice,
these multiple evaluation metrics can be used in two
ways. The first way defines a loss function by
aggregating multiple metrics, while the second way
defines a multiobjective loss function by considering
each metric as an objective function. In this paper, we
propose a new way to use multiple evaluation metrics,
which is different from the aggregating method and the
mutliobjective method. Our method is based on genetic
programming. The idea is to randomly use one metric in
each iteration of the selection operator. Therefore,
multiple metrics can be used alternatively in the
running process. To validate the effectiveness of our
new approach, we conduct experiments on ten benchmark
datasets. The experimental results show that the new
approach can improve the population diversity, and can
achieve the performance better than or similar to that
of the traditional symbolic regression algorithms.",
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keywords = "genetic algorithms, genetic programming, Measurement,
Heuristic algorithms, Sociology, Benchmark testing,
Linear programming, Statistics, symbolic regression,
multi-metric selection",
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DOI = "doi:10.1109/SMC42975.2020.9283385",
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ISSN = "2577-1655",
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month = oct,
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notes = "Also known as \cite{9283385}",
- }
Genetic Programming entries for
Hu Zhang
Hengzhe Zhang
Aimin Zhou
Citations