An Improved Single Node Genetic Programming for Symbolic Regression

Jiří Kubalík, Robert Babuška

Abstract

This paper presents a first step of our research on designing an effective and efficient GP-based method for solving the symbolic regression. We have proposed three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on the depth of the nodes, (2) operators for placing a compact version of the best tree to the beginning and to the end of the population, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on three symbolic regression problems and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to significantly improve the performance of the SNGP algorithm.

References

  1. Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2):87-129.
  2. Jackson, D. (2012a). A new, node-focused model for genetic programming. In EuroGP 2012, pages 49-60.
  3. Jackson, D. (2012b). Single node genetic programming on problems with side effects. In PPSN XII, pages 327- 336.
  4. Koza, J. (1992). Genetic programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, 2nd edition.
  5. McDermott, J. e. a. (2012). Genetic programming needs better benchmarks. In Proceedings of the GECCO 7812, pages 791-798, New York, NY, USA. ACM.
  6. Miller, J. and Thomson, P. (2000). Cartesian genetic programming. In Poli, R. et al. (eds.) EuroGP 2000, LNCS, vol. 1802, pp. 121-132 . Springer.
  7. Ryan, C. and Azad, R. M. A. (2014). A simple approach to lifetime learning in genetic programmingbased symbolic regression. Evolutionary Computation, 22(2):287-317.
  8. Ryan, C., Collins, J., Collins, J., and O'Neill, M. (1998). Grammatical evolution: Evolving programs for an arbitrary language. In LNCS 1391, Proceedings of the First European Workshop on Genetic Programming, pages 83-95. Springer-Verlag.
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Paper Citation


in Harvard Style

Kubalík J. and Babuška R. (2015). An Improved Single Node Genetic Programming for Symbolic Regression . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 244-251. DOI: 10.5220/0005598902440251


in Bibtex Style

@conference{ecta15,
author={Jiří Kubalík and Robert Babuška},
title={An Improved Single Node Genetic Programming for Symbolic Regression},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={244-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005598902440251},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - An Improved Single Node Genetic Programming for Symbolic Regression
SN - 978-989-758-157-1
AU - Kubalík J.
AU - Babuška R.
PY - 2015
SP - 244
EP - 251
DO - 10.5220/0005598902440251