Evolving autoencoding structures through genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Rodriguez-Coayahuitl:GPEM:autoencoding,

• author = "Lino Rodriguez-Coayahuitl and Alicia Morales-Reyes and Hugo Jair Escalante",
• title = "Evolving autoencoding structures through genetic programming",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2019",
• volume = "20",
• number = "3",
• pages = "413--440",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Evolutionary machine learning, Representation learning, Autoencoder",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-019-09354-4",
• size = "28 pages",
• abstract = "We propose a novel method to evolve autoencoding structures through genetic programming (GP) for representation learning on high dimensional data. It involves a partitioning scheme of high dimensional input representations for distributed processing as well as an on-line form of learning that allows GP to efficiently process training datasets composed of hundreds or thousands of samples. The use of this on-line learning approach has important consequences in computational cost given different evolutionary population dynamics, namely steady state evolution and generational replacement. We perform a complete experimental study to compare the evolution of autoencoders (AEs) under different population dynamics and genetic operators useful to evolve GP based AEs individuals. Also, we compare the performance of GP based AEs with another representation learning method. Competitive results have been achieved through the proposed method.",

}

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

Citations