Optimization Networks for Integrated Machine Learning

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

@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