Towards Deep Representation Learning with Genetic Programming

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@Misc{DBLP:journals/corr/abs-1802-07133,

• author = "Lino Rodriguez-Coayahuitl and Alicia Morales-Reyes and Hugo Jair Escalante",
• title = "Towards Deep Representation Learning with Genetic Programming",
• howpublished = "arXiv",
• year = "2018",
• month = "20 " # feb,
• keywords = "genetic algorithms, genetic programming, ANN",
• URL = "http://arxiv.org/abs/1802.07133",
• biburl = "https://dblp.org/rec/bib/journals/corr/abs-1802-07133",
• abstract = "Genetic Programming (GP) is an evolutionary algorithm commonly used for machine learning tasks. In this paper we present a method that allows GP to transform the representation of a large-scale machine learning dataset into a more compact representation, by means of processing features from the original representation at individual level. We develop as a proof of concept of this method an autoencoder. We tested a preliminary version of our approach in a variety of well-known machine learning image datasets. We speculate that this method, used in an iterative manner, can produce results competitive with state-of-art deep neural networks.",
• notes = "See also Technical report CCC-17-009 January 17, 2018 46 pages",

}

Genetic Programming entries for Lino Rodriguez-Coayahuitl Alicia Morales-Reyes Hugo Jair Escalante

Citations