Balanced Cartesian Genetic Programming via migration and opposition-based learning: application to symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4215

  author =       "Samaneh Yazdani and Jamshid Shanbehzadeh",
  title =        "Balanced Cartesian Genetic Programming via migration
                 and opposition-based learning: application to symbolic
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "2",
  pages =        "133--150",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Biogeography-based optimisation,
                 Migration, Opposition-based learning, Exploration
                 exploitation trading-off",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-014-9230-4",
  size =         "18 pages",
  abstract =     "The exploration exploitation trade-off is an important
                 aspect of evolutionary algorithms which determines the
                 efficiency and accuracy of these algorithms. Cartesian
                 Genetic Programming (CGP) is a generalisation of the
                 graph based genetic programming. It is implemented with
                 mutation only and does not have any possibility to
                 share information among solutions. The main goal of
                 this paper is to present an effective method for
                 balancing the exploration and exploitation of CGP
                 referred to as Balanced Cartesian Genetic Programming
                 (BCGP) by incorporating distinctive features from bio
                 geography-based optimisation (BBO) and opposition-based
                 learning. To achieve this goal, we apply BBO's
                 migration operator without considering any
                 modifications in the representation of CGP. This
                 operator has good exploitation ability and can be used
                 to share information among individuals in CGP. In
                 addition, in order to improve the exploration ability
                 of CGP, a new mutation operator is integrated into CGP
                 inspired from the concept of opposition-based learning.
                 Experiments have been conducted on symbolic regression.
                 The experimental results show that the proposed BCGP
                 method outperforms the traditional CGP in terms of
                 accuracy and the convergence speed.",
  notes =        "1. Department of Computer Engineering, Science and
                 Research Branch, Islamic Azad University, Tehran, Iran
                 2. Department of Computer Engineering, Faculty of
                 Engineering, Kharazmi University, Tehran, Iran


Genetic Programming entries for Samaneh Yazdani Jamshid Shanbe Zadeh