A Population Diversity-Oriented Gene Expression Programming for Function Finding

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

@InProceedings{conf/seal/LiuLLJ10,

• title = "A Population Diversity-Oriented Gene Expression Programming for Function Finding",
• author = "Ruochen Liu and Qifeng Lei and Jing Liu and Licheng Jiao",
• booktitle = "8th International Conference on Simulated Evolution and Learning (SEAL 2010)",
• year = "2010",
• volume = "6457",
• editor = "Kalyanmoy Deb and Arnab Bhattacharya and Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and Joydeep Dutta and Santosh K. Gupta and Ashu Jain and Varun Aggarwal and J{\"u}rgen Branke and Sushil J. Louis and Kay Chen Tan",
• series = "Lecture Notes in Computer Science",
• pages = "215--219",
• address = "Kanpur, India",
• month = dec # " 1-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, gene expression programming",
• bibdate = "2010-12-01",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2010.html#LiuLLJ10",
• isbn13 = "978-3-642-17297-7",
• DOI = "doi:10.1007/978-3-642-17298-4",
• abstract = "Gene expression programming (GEP) is a novel evolutionary algorithm, which combines the advantages of simple genetic algorithm (SGA) and genetic programming (GP). Owing to its special structure of linear encoding and nonlinear decoding, GEP has been applied in various fields such as function finding and data classification. In this paper, we propose a modified GEP (Mod-GEP), in which, two strategies including population updating and population pruning are used to increase the diversity of population. Mod-GEP is applied into two practical function finding problems, the results show that Mod-GEP can get a more satisfactory solution than that of GP, GEP and GEP based on statistical analysis and stagnancy (AMACGEP",

}

Genetic Programming entries for Ruochen Liu Qifeng Lei Jing Liu Licheng Jiao

Citations