Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal
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- @InProceedings{trujillo:2018:GPTP,
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author = "Leonardo Trujillo and Luis Munoz and Uriel Lopez and
Daniel E. Hernandez",
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title = "Untapped Potential of Genetic Programming: Transfer
Learning and Outlier Removal",
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booktitle = "Genetic Programming Theory and Practice XVI",
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year = "2018",
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editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman",
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pages = "193--207",
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address = "Ann Arbor, USA",
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month = "17-20 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-030-04734-4",
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URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_10",
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DOI = "doi:10.1007/978-3-030-04735-1_10",
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abstract = "In the era of Deep Learning and Big Data, the place of
Genetic Programming (GP) within the Machine Learning
area seems difficult to define. Whether it is due to
technical constraints or conceptual barriers, GP is
currently not a paradigm of choice for the development
of state-of-the-art machine learning systems.
Nonetheless, there are important features of the GP
approach that make it unique and should continue to be
actively explored and studied. In this work we focus on
two aspects of GP that have previously received little
or no attention, particularly in tree-based GP for
symbolic regression. First, on the potential of GP to
perform transfer learning, where solutions evolved for
one problem are transferred to another. Second, on the
potential of GP individuals to detect the true
underlying structure of an input dataset and detect
anomalies in the input data, what are known as
outliers. This work presents initial results on both
issues, with the goal of fostering discussion and
showing that there is still untapped potential in the
GP paradigm.",
- }
Genetic Programming entries for
Leonardo Trujillo
Luis Munoz Delgado
Uriel Lopez
Daniel Eduardo Hernandez Morales
Citations