Towards Evolutionary Machine Learning Comparison, Competition, and Collaboration with a Multi-cloud Platform

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Salza:2017:GECCO,
  author =       "Pasquale Salza and Erik Hemberg and 
                 Filomena Ferrucci and Una-May O'Reilly",
  title =        "Towards Evolutionary Machine Learning Comparison,
                 Competition, and Collaboration with a Multi-cloud
                 Platform",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1263--1270",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  keywords =     "genetic algorithms, genetic programming, cloud
                 computing, evolutionary machine learning,
                 microservices",
  URL =          "http://doi.acm.org/10.1145/3067695.3082474",
  DOI =          "doi:10.1145/3067695.3082474",
  acmid =        "3082474",
  size =         "8 pages",
  abstract =     "We present cCube, an open source architecture used to
                 automatically create an application of one or more
                 Evolutionary Machine Learning (EML) classification
                 algorithms that can be deployed to the cloud with
                 automatic data factorization, training, result
                 filtering and fusion. cCube enables automated EML
                 classification algorithms comparison, competition and
                 multi-party collaboration. It can be used by an
                 algorithm developer, a community working together or a
                 black box user of EML classification. It requires
                 minimal extra code to cloud-scale shared-memory
                 implementations. It employs a microservices
                 architecture and software containers into which user
                 code is integrated allowing to access to the full
                 benefits of cloud computing, e.g., on demand and
                 elastic computing, while not committing (code or
                 patronage) to a specific cloud provider such as Amazon
                 Web Services or OpenStack. We demonstrate cCube,
                 straddling our application across two different cloud
                 providers and replicate the collaborative activity at
                 zero cost.",
  notes =        "Also known as \cite{Salza:2017:TEM:3067695.3082474}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Pasquale Salza Erik Hemberg Filomena Ferrucci Una-May O'Reilly

Citations