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. © 2013 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Qi, F., Ma, Y., Liu, X., & Ji, G. (2013). A hybrid genetic programming with particle swarm optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7929 LNCS, pp. 11–18). Springer Verlag. https://doi.org/10.1007/978-3-642-38715-9_2
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