Abstract
By changing the linear encoding and redefining the evolving rules, particle swarm algorithm is introduced into genetic programming and an hybrid genetic programming with particle swarm optimization (HGPPSO) is proposed. The performance of the proposed algorithm is tested on tow symbolic regression problem in genetic programming and the simulation results show that HGPPSO is better than genetic programming in both convergence times and average convergence generations and is a promising hybrid genetic programming algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Luke, S.: Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation 4(3), 274–283 (2000)
Kushchu, I.: Genetic programming and evolutionary genralization. IEEE Transactions on Evolutionary Computation 6(5), 431–442 (2002)
Gustafson, S., Kendall, G.: Diversity in genetic programming an analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation 8(1), 47–62 (2004)
Yun-Seog, Y., Won-Sun, R., Young-Soon, Y., Nam-Joon, K.: Implementing linear models in genetic programming. IEEE Transactions on Evolutionary Computation 8(6), 542–566 (2004)
Nguyen, X.H., McKay, R.I., Essam, E.: Representation and structural difficulty in genetic programming. IEEE Transactions on Evolutionary Computation 10(2), 157–166 (2006)
Uy, N.Q., Hien, N.T., Hoai, N.X., O’Neill, M.: Improving the generalisation ability of genetic programming with semantic similarity based crossover. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 184–195. Springer, Heidelberg (2010)
Castelli, M., Manzoni, L., Silva, S., Vanneschi, L.: A quantitative Study of Learning and Generalization in Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 25–36. Springer, Heidelberg (2011)
Muni, D.P., Pal, N.R., Das, J.: A novel approach to design classifiers using genetic programming. IEEE Transactions on Evolutionary Computation 8(2), 183–196 (2004)
Pedro, G.E., Sebastian, V., Francisco, H.: A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man, and Cybernetics 40(2), 121–144 (2010)
Hajira, J., Abdul, R.B.: Review of classification using genetic programming. International Journal of Engineering Science and Technology 2(2), 94–103 (2010)
Qi, F., Liu, X.Y., Ma, Y.H.: Synthesis of neural tree models by improved breeder genetic programming. Neural Computing and Application 3, 515–521 (2012)
Neal, W., Zbigniew, M., Moutaz, J.K., Rob, R.M.: Time series forecasting for dynamic environments the DyFor genetic program model. IEEE Transactions on Evolutionary Computation 11(4), 433–452 (2007)
Emiliano, C.J.: Long memory time series forecasting by using genetic programming. Genet. Program Evolvable Mach. 12, 429–456 (2011)
Bartoli, A., Davanzo, G., De Lorenzo, A., Medvet, E.: GP-Based electricity price forecasting. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 37–48. Springer, Heidelberg (2011)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. Perth (1995)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation 8, 204–210 (2004)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man and Cybernetics - Part B 3(6), 1272–1282 (2005)
Liang, J.J., Qin, A.K., Suganthan, P.N., et al.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qi, F., Ma, Y., Liu, X., Ji, G. (2013). A Hybrid Genetic Programming with Particle Swarm Optimization. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-38715-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
eBook Packages: Computer ScienceComputer Science (R0)