A Hybrid Genetic Programming with Particle Swarm Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/swarm/QiMLJ13,

• author = "Feng Qi and Yinghong Ma and Xiyu Liu and Guangyong Ji",
• title = "A Hybrid Genetic Programming with Particle Swarm Optimization",
• booktitle = "Proceedings 4th International Conference on Advances in Swarm Intelligence, ICSI 2013, Part {II}",
• year = "2013",
• editor = "Ying Tan and Yuhui Shi and Hongwei Mo",
• volume = "7929",
• series = "Lecture Notes in Computer Science",
• pages = "11--18",
• address = "Harbin, China",
• month = jun # " 12-15",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, PSO",
• isbn13 = "978-3-642-38714-2",
• URL = "http://dx.doi.org/10.1007/978-3-642-38715-9",
• DOI = "doi:10.1007/978-3-642-38715-9_2",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/swarm/icsi2013-2.html#QiMLJ13",
• size = "8 pages",
• 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 optimisation (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.",

}

Genetic Programming entries for Feng Qi Yinghong Ma Xiyu Liu Guangyong Ji

Citations