Genetic Programming Based Evolvement of Models of Models

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Number of pages9
ISBN (Print)9783030450922
Publication statusPublished - 2020
Event17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 - Las Palmas de Gran Canaria, Spain
Duration: 17 Feb 201922 Feb 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12013 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
CityLas Palmas de Gran Canaria


  • Genetic Programming
  • Models of Models
  • Symbolic Regression


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