Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Lu:2016:CIN,

• title = "Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem",
• author = "Qiang Lu and Jun Ren and Zhiguang Wang",
• journal = "Computational Intelligence and Neuroscience",
• year = "2016",
• pages = "Article ID 1021378",
• keywords = "genetic algorithms, genetic programming",
• publisher = "Hindawi Publishing Corporation",
• bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov",
• identifier = "/pmc/articles/PMC4706865/",
• language = "en",
• oai = "oai:pubmedcentral.nih.gov:4706865",
• rights = "Copyright 2016 Qiang Lu et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.",
• URL = "http://dx.doi.org/10.1155/2016/1021378",
• URL = "http://downloads.hindawi.com/journals/cin/2016/1021378.pdf",
• size = "18 pages",
• abstract = "A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalised to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.",

}

Genetic Programming entries for Qiang Lu Jun Ren Zhiguang Wang

Citations