Genetic Programming Based Evolvement of Models of Models

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

@InProceedings{Semenkina:2019:EUROCAST,

• author = "Mariia Semenkina and Bogdan Burlacu and Michael Affenzeller",
• title = "Genetic Programming Based Evolvement of Models of Models",
• booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019",
• year = "2019",
• editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia",
• volume = "12013",
• series = "Lecture Notes in Computer Science",
• pages = "387--395",
• address = "Las Palmas de Gran Canaria, Spain",
• month = "17-22 " # feb,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Symbolic Regression, Models of Models",
• isbn13 = "978-3-030-45092-2",
• DOI = "doi:10.1007/978-3-030-45093-9_47",
• abstract = "The main idea of this paper is to use Simple Symbolic Formulas generated offline with the help of the deterministic function extraction algorithm as building blocks for Genetic Programming. This idea comparison to Automatically Defined Functions approach was considered. A possibility to take into consideration an expert's knowledge about the problem in hand has been reviewed. In this work a map of building block's set is generated by means of clustering. All distances between blocks are calculated offline by using a special metric for symbolic expressions. A mutation operator in Genetic Programming was modified for work with this kind of nodes. The effectiveness of this approach was evaluated on benchmark as well as on real world problems.",

}

Genetic Programming entries for Mariia Semenkina Bogdan Burlacu Michael Affenzeller

Citations