Abstract
This paper focuses on the use of hybrid genetic programming for the supervised machine learning method called symbolic regression. While the basic version of GP symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. Choice of local learning method can accelerate the evolution, but it also has its disadvantages in the form of additional costs. Strong local learning can inhibit the evolutionary search for the optimal genotype due to the hiding effect, in which the fitness of the individual only slightly depends on his inherited genes. This paper aims to compare the Lamarckian and Baldwinian approaches to the lifetime adaptation of individuals and their influence on the rate of evolution in the search for function, which fits the given input-output data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Iba, H., Sato, T., de Garis, H.: Recombination guidance for numerical genetic programming. In: Proceedings of 1995 IEEE International Conference on Evolutionary Computation, p. 97. IEEE (1995). https://doi.org/10.1109/icec.1995.489292. http://ieeexplore.ieee.org/document/489292/. ISBN 0-7803-2759-4
Nikolaev, N.Y., Iba, H.: Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods. Springer, New York (2006). ISBN 978-0-387-31239-2
Schoenauer, M., Lamy, B., Jouve, F.: Identification of mechanical behaviour by genetic programming part II: energy formulation. Technical report, Ecole Polytechnique, 91128 Palaiseau, France (1995)
Sharman, K.C., Esparcia-Alcazar, A.I., Li, Y.: Evolving signal processing algorithms by genetic programming. In: Zalzala, A.M.S. (ed.) First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, Sheffield, UK, 12–14 September 1995, vol. 414, pp. 473–480. IEE (1995). http://www.iti.upv.es/~anna/papers/galesi95.ps. ISBN 0-85296-650-4
Whitley, D., Gordon, S., Mathias, K.: Lamarckian evolution, the Baldwin effect and function optimization. In: Davidor, Y., Schwefel, H.P., Manner, R. (eds.) Parallel Problem Solving from Nature - PPSN III, pp. 6–15. Springer, Berlin (1994)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. Bradford Book, Cambridge (1992). ISBN 0-262-11170-5
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Press, Burton (2008). ISBN 978-1-4092-0073-4
Le, N., Brabazon, A., O’Neill, M.: How the “baldwin effect” can guide evolution in dynamic environments. In: Fagan, D., Martín-Vide, C., O’Neill, M., Vega-Rodríguez, M. (eds.) Theory and Practice of Natural Computing. Lecture Notes in Computer Science, pp. 164–175. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04070-3_13. ISBN 978-3-030-04069-7
Reďko, V.G., Mosalov, O.P., Prokhorov, D.V.: A model of evolution and learning. Neural Networks 18(5–6), 738–745 (2005). https://doi.org/10.1016/j.neunet.2005.06.005. https://linkinghub.elsevier.com/retrieve/pii/S0893608005001358. ISSN 08936080
Turney, P.D.: Myths and legends of the Baldwin effect. arXiv preprint cs/0212036 (2002)
Anderson, R.W.: Learning and evolution: a quantitative genetics approach. J. Theor. Biol. 175(1), 89–101 (1995). https://doi.org/10.1006/jtbi.1995.0123. http://linkinghub.elsevier.com/retrieve/pii/S0022519385701233. ISSN 00225193
Hinton, G.E., Nowlan, S.J.: How learning can guide evolution. Complex Syst. 1, 495–502 (1987)
French, R., Messinger, A.: Genes, phenes and the Baldwin effect: learning and evolution in a simulated population. In: Brooks, R., Maes, P. (eds.) Artificial Life IV. MIT Press, Cambridge (1994)
Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson Education, Harlow (2014). ISBN 978-1-29202-420-2
Ackley, D.H.: Stochastic iterated genetic hill-climbing. Doctoral dissertation, Carnegie-Mellon University, Pittsburgh (1987)
Acknowledgments
The work has been supported by the Funds of University of Pardubice (by project “SGS 2019” No: SGS_2019_021), Czech Republic. This support is very gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Merta, J., Brandejský, T. (2019). Lifetime Adaptation in Genetic Programming for the Symbolic Regression. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-31362-3_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31361-6
Online ISBN: 978-3-030-31362-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)