Optimization Networks for Integrated Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{6344,
  author =       "Michael Kommenda and Johannes Karder and 
                 Andreas Beham and Bogdan Burlacu and Gabriel K. Kronberger and 
                 Stefan Wagner and Michael Affenzeller",
  title =        "Optimization Networks for Integrated Machine
                 Learning",
  booktitle =    "Computer Aided Systems Theory, EUROCAST 2017",
  year =         "2017",
  editor =       "Roberto Moreno-Diaz and Franz Pichler and 
                 Alexis Quesada-Arencibia",
  volume =       "10671",
  series =       "Lecture Notes in Computer Science",
  pages =        "392--399",
  address =      "Las Palmas de Gran Canaria, Spain",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Optimization
                 networks, Machine learning, Feature selection,
                 Optimization analysis",
  isbn13 =       "978-3-319-74718-7",
  URL =          "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_47",
  DOI =          "doi:10.1007/978-3-319-74718-7_47",
  abstract =     "Optimization networks are a new methodology for
                 holistically solving interrelated problems that have
                 been developed with combinatorial optimization problems
                 in mind. In this contribution we revisit the core
                 principles of optimization networks and demonstrate
                 their suitability for solving machine learning
                 problems. We use feature selection in combination with
                 linear model creation as a benchmark application and
                 compare the results of optimization networks to
                 ordinary least squares with optional elastic net
                 regularization. Based on this example we justify the
                 advantages of optimization networks by adapting the
                 network to solve other machine learning problems.
                 Finally, optimization analysis is presented, where
                 optimal input values of a system have to be found to
                 achieve desired output values. Optimization analysis
                 can be divided into three subproblems: model creation
                 to describe the system, model selection to choose the
                 most appropriate one and parameter optimization to
                 obtain the input values. Therefore, optimization
                 networks are an obvious choice for handling
                 optimization analysis tasks.",
  notes =        "Published 2018?",
}

Genetic Programming entries for Michael Kommenda Johannes Karder Andreas Beham Bogdan Burlacu Gabriel Kronberger Stefan Wagner Michael Affenzeller

Citations