Artificial bee colony programming for symbolic regression

  • Authors:

  • Dervis Karaboga;Celal Ozturk;Nurhan Karaboga;Beyza Gorkemli

  • Affiliations:

  • Erciyes University, Engineering Faculty, Intelligent Systems Research Group, Kayseri, Turkiye;Erciyes University, Engineering Faculty, Intelligent Systems Research Group, Kayseri, Turkiye;Erciyes University, Engineering Faculty, Intelligent Systems Research Group, Kayseri, Turkiye;Erciyes University, Engineering Faculty, Intelligent Systems Research Group, Kayseri, Turkiye

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

Artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms. It has been introduced in 2005 and applied in several fields to solve different problems up to date. In this paper, an artificial bee colony algorithm, called as Artificial Bee Colony Programming (ABCP), is described for the first time as a new method on symbolic regression which is a very important practical problem. Symbolic regression is a process of obtaining a mathematical model using given finite sampling of values of independent variables and associated values of dependent variables. In this work, a set of symbolic regression benchmark problems are solved using artificial bee colony programming and then its performance is compared with the very well-known method evolving computer programs, genetic programming. The simulation results indicate that the proposed method is very feasible and robust on the considered test problems of symbolic regression.